Undermind Search: research articles about how search query time (of a system such as google scholar) affects user experience and usage.

Research Topic

research articles about how search query time (of a system such as google scholar) affects user experience and usage.

Summary

The literature search has identified several studies directly addressing how search query time affects user experience and usage in online search systems akin to Google Scholar [4, 6, 13, 15, 16, 33].

These studies highlight significant implications of search latency on user behavior, satisfaction, and stress levels. For instance, increased query time has been shown to influence user preference, leading to potential abandonment or reduced interaction with the system [4, 6]. Papers such as [13] and [15] offer detailed experimental results demonstrating how system delays under time constraints impact users' search strategies and document engagement. Moreover, research like [16] and [33] delineates the physiological and behavioral adjustments users make in response to system delays, emphasizing the critical nature of optimizing search speed to enhance overall user experience. These findings underscore the importance of minimizing search query times to maintain and improve user interaction with systems.

To understand the relationships and patterns within the papers found, see also:

Direct Impact of Search Query Time on User Experience

Papers that explicitly examine how search query time affects user experience and usage in online search systems resembling Google Scholar. References: [4]

In-depth Analysis of Search Latency Effects on User Behavior

Studies rigorously exploring how different aspects of search latency influence user behavior, engagement, and satisfaction. References: [1, 2, 7, 10, 11, 16, 21, 31]

Search Interface and System Delays

Research investigating how system performance and delays impact user satisfaction, stress response, and behavior during searches. References: [6, 8, 13, 15, 16, 33, 35]

User Coping Mechanisms and Perceptions under Time Pressure

Studies focusing on how perceptions and users' adjustments to time constraints or perceived delays affect their interactions with search systems. References: [23, 25, 26, 33]

So far, I've closely analyzed 460 of the most promising papers, and I've found ~17-45 that are relevant, which is probably more than 90% of all that exist.

To get this estimate, we do a statistical analysis of the discovery process.

References

Last 5 years

Last 2 years

> 1 citation per year

> 5 citations per year

Topic Match

Cit./Year

Year

Paper

Paper Relevance Summary

97.9%

12.5

2014

[1] Impact of response latency on user behavior in web search Ioannis Arapakis, ..., and B. B. Cambazoglu Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval 2014 - 126 citations - Show abstract - Cite 97.9% topic match

Provides insights into how response latency affects web search user behavior. Demonstrates through a controlled study and analysis of Yahoo search logs that increased latency influences user click behavior. Specifically shows users are more likely to notice delays in faster systems and prefer pages with lower latency.

Provides insights into how response latency affects web search user behavior. Demonstrates through a controlled study and analysis of Yahoo search logs that increased latency influences user click behavior. Specifically shows users are more likely to notice delays in faster systems and prefer pages with lower latency.

96.8%

2.2

2017

[2] Understanding and Leveraging the Impact of Response Latency on User Behaviour in Web Search Xiao Bai, ..., and Ana Freire ACM Transactions on Information Systems (TOIS) 2017 - 15 citations - Show abstract - Cite 96.8% topic match

Investigates the impact of response latency on web search behavior. Conducted study showing increased sensitivity to delays in users of fast versus slow search systems. Highlights the nuanced relationship between search system speed and user experience.

Investigates the impact of response latency on web search behavior. Conducted study showing increased sensitivity to delays in users of fast versus slow search systems. Highlights the nuanced relationship between search system speed and user experience.

96.3%

1.9

2012

[3] Situation Normality and the Shape of Search: The Effects of Time Delays and Information Presentation on Search Behavior Nolan J. Taylor, ..., and Jeff W. Cummings Cognition & the Arts eJournal 2012 - 23 citations - Show abstract - Cite 96.3% topic match

Explores the impact of time delays on search behavior. Demonstrates that delays up to 7-11 seconds increase page engagement without reducing data searched. Suggests a shift in search depth rather than search abandonment, tying closely to search query time and user experience.

Explores the impact of time delays on search behavior. Demonstrates that delays up to 7-11 seconds increase page engagement without reducing data searched. Suggests a shift in search depth rather than search abandonment, tying closely to search query time and user experience.

95.6%

0.0

2010

[4] A Study on User Information Seeking Behavior of Metasearch System in the Academic Library Y. Nam and Ji-Ann Yang Journal of The Korean Society for Information Management 2010 - 0 citations - Show abstract - Cite 95.6% topic match

Examines user preference for Google Scholar in academic libraries. Highlights Google Scholar's speed and simplicity as key factors for its use over other e-resources. Suggests that search query time significantly impacts user choice and behavior.

Examines user preference for Google Scholar in academic libraries. Highlights Google Scholar's speed and simplicity as key factors for its use over other e-resources. Suggests that search query time significantly impacts user choice and behavior.

94.1%

0.2

2002

[5] The direct and interactive effects of web site delay length, delay variability, and feedback on user attitudes, performance, and intentions D. Galletta and P. Polak Journal Not Provided 2002 - 4 citations - Show abstract - Cite 94.1% topic match

Investigates effects of web delay on user attitudes and performance. Explores delay length, delay variability, and user feedback. Directly relevant to understanding effects of search query time on user experience.

Investigates effects of web delay on user attitudes and performance. Explores delay length, delay variability, and user feedback. Directly relevant to understanding effects of search query time on user experience.

94.1%

2.9

2014

[6] Stuck in traffic: how temporal delays affect search behaviour David Maxwell and L. Azzopardi Proceedings of the 5th Information Interaction in Context Symposium 2014 - 29 citations - Show abstract - Cite 94.1% topic match

Investigates how search delays affect user interaction. Examines user behavior under query response and document download delays. Employs Information Foraging Theory and Search Economic Theory, contrasts previous works.

Investigates how search delays affect user interaction. Examines user behavior under query response and document download delays. Employs Information Foraging Theory and Search Economic Theory, contrasts previous works.

93.3%

0.0

2014

[7] A Controlled User Study on Human Perception of Web Search Latency ∗ Ioannis Arapakis and Xiao Bai Journal Not Provided 2014 - 0 citations - Show abstract - Cite 93.3% topic match

Examines the impact of web search latency on user behavior. Studies user sensitivity to latency and its effect on engagement and satisfaction. Also explores bias from search site branding, adding a unique perspective on user perception.

Examines the impact of web search latency on user behavior. Studies user sensitivity to latency and its effect on engagement and satisfaction. Also explores bias from search site branding, adding a unique perspective on user perception.

90.1%

3.6

2003

[8] A comprehensive and systematic model of user evaluation of Web search engines: II. An evaluation by undergraduates Louise T. Su J. Assoc. Inf. Sci. Technol. 2003 - 74 citations - Show abstract - Cite 90.1% topic match

Evaluates user satisfaction with search engine response time. Compared user experience metrics across different web search engines. Focused on various performance measures including efficiency and utility.

Evaluates user satisfaction with search engine response time. Compared user experience metrics across different web search engines. Focused on various performance measures including efficiency and utility.

89.9%

2.3

2014

[9] Online search stopping behaviors: An investigation of query abandonment and task stopping Wan-Ching Wu and D. Kelly ASIS&T Annual Meeting 2014 - 24 citations - Show abstract - Cite 89.9% topic match

Investigates factors influencing query abandonment during searches. Shows decision to abandon a query is influenced by search results and task properties. Focuses more broadly on search behavior, not directly on search query time effects.

Investigates factors influencing query abandonment during searches. Shows decision to abandon a query is influenced by search results and task properties. Focuses more broadly on search behavior, not directly on search query time effects.

88.6%

2.9

2015

[10] Unconscious Physiological Effects of Search Latency on Users and Their Click Behaviour Miguel Barreda-Ángeles, ..., and Alexandre Pereda-Baños Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval 2015 - 26 citations - Show abstract - Cite 88.6% topic match

Investigates the physiological impact of search latency on users. Reveals increased latency effects such as nuanced attentional and emotional reactions. Analyzes users' click behavior changes in response to varied latency levels.

Investigates the physiological impact of search latency on users. Reveals increased latency effects such as nuanced attentional and emotional reactions. Analyzes users' click behavior changes in response to varied latency levels.

87.2%

0.0

2006

[11] Information Search on the Web: Understanding the Impact of Response Time Delays with Information Foraging Theory Nolan J. Taylor and A. Dennis Social Science Research Network 2006 - 0 citations - Show abstract - Cite 87.2% topic match

Investigates the impact of response time delays on web search behavior. Shows that delays lead users to reduce information search, impacting decision quality. Uses Information Foraging Theory to explain user reactions to web search delays.

Investigates the impact of response time delays on web search behavior. Shows that delays lead users to reduce information search, impacting decision quality. Uses Information Foraging Theory to explain user reactions to web search delays.

85.7%

1.3

2007

[12] Uncovering the Effect of System Performance on User Behavior from Traces of Parallel Systems Edi Shmueli and D. Feitelson 2007 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems 2007 - 21 citations - Show abstract - Cite 85.7% topic match

Investigates system performance impact on user behavior. Found user behavior correlated with system response times, influencing session continuation or abandonment. Although focused on parallel systems, findings potentially applicable to broader system types including search systems.

Investigates system performance impact on user behavior. Found user behavior correlated with system response times, influencing session continuation or abandonment. Although focused on parallel systems, findings potentially applicable to broader system types including search systems.

85.3%

4.9

2016

[13] Impacts of Time Constraints and System Delays on User Experience Anita Crescenzi, ..., and L. Azzopardi Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval 2016 - 41 citations - Show abstract - Cite 85.3% topic match

Provides insights on system delays affecting user experience. Studies impacts of time constraints and system delays during searches. Utilizes a structured experiment with forty-five participants, offering robust data.

Provides insights on system delays affecting user experience. Studies impacts of time constraints and system delays during searches. Utilizes a structured experiment with forty-five participants, offering robust data.

84.5%

0.4

2017

[14] Managing Tail Latencies in Large Scale IR Systems J. Mackenzie Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval 2017 - 3 citations - Show abstract - Cite Abstract: With the growing popularity of the world-wide-web and the increasing accessibility of smart devices, data is being generated at a faster rate than ever before. This presents scalability challenges to web-scale search systems -- how can we efficiently index, store and retrieve such a vast amount of data? A large amount of prior research has attempted to address many facets of this question, with the invention of a range of efficient index storage and retrieval frameworks that are able to efficiently answer most queries. However, the current literature generally focuses on improving the mean or median query processing time in a given system. In the proposed PhD project, we focus on improving the efficiency of high percentile tail latencies in large scale IR systems while minimising end-to-end effectiveness loss. Although there is a wealth of prior research involving improving the efficiency of large scale IR systems, the most relevant prior work involves predicting long-running queries and processing them in various ways to avoid large query processing times. Prediction is often done through pre-trained models based on both static and dynamic features from queries and documents. Many different approaches to reducing the processing time of long running queries have been proposed, including parallelising queries that are predicted to run slowly, scheduling queries based on their predicted run time, and selecting or modifying the query processing algorithm depending on the load of the system. Considering the specific focus on tail latencies in large-scale IR systems, the proposed research aims to: (i) study what causes large tail latencies to occur in large-scale web search systems, (ii) propose a framework to mitigate tail latencies in multi-stage retrieval through the prediction of a vast range of query-specific efficiency parameters, (iii) experiment with mixed-mode query semantics to provide efficient and effective querying to reduce tail latencies, and (iv) propose a time-bounded solution for Document-at-a-Time (DaaT) query processing which is suitable for current web search systems. As a preliminary study, Crane et al. compared some state-of-the-art query processing strategies across many modern collections. They found that although modern DaaT dynamic pruning strategies are very efficient for ranked disjunctive processing, they have a much larger variance in processing times than Score-at-a-Time (SaaT) strategies which have a similar efficiency profile regardless of query length or the size of the required result set. Furthermore, Mackenzie et al. explored the efficiency trade-offs for paragraph retrieval in a multi-stage question answering system. They found that DaaT dynamic pruning strategies could efficiently retrieve the top-1,000 candidate paragraphs for very long queries. Extending on prior work, Mackenzie et al. showed how a range of per-query efficiency settings can be accurately predicted such that 99.99 percent of queries are serviced in less than 200 ms without noticeable effectiveness loss. In addition, a reference list framework was used for training models such that no relevance judgements or annotations were required. Future work will focus on improving the candidate generation stage in large-scale multi-stage retrieval systems. This will include further exploration of index layouts, traversal strategies, and query rewriting, with the aim of improving early stage efficiency to reduce the system tail latency, while potentially improving end-to-end effectiveness. 84.5% topic match

Focuses on managing high percentile tail latencies in IR systems. Proposes methods to predict and reduce long query processing times. Relevant for studying impacts of delayed search responses on user experience.

Focuses on managing high percentile tail latencies in IR systems. Proposes methods to predict and reduce long query processing times. Relevant for studying impacts of delayed search responses on user experience.

79.8%

3.8

2015

[15] Time Pressure and System Delays in Information Search Anita Crescenzi, ..., and L. Azzopardi Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval 2015 - 34 citations - Show abstract - Cite 79.8% topic match

Shows impact of time pressure and system delays on search behavior. Under time pressure, users showed increased query rates and less document engagement. Lacks specific focus on search query time's direct effect on user experience in systems like Google Scholar.

Shows impact of time pressure and system delays on search behavior. Under time pressure, users showed increased query rates and less document engagement. Lacks specific focus on search query time's direct effect on user experience in systems like Google Scholar.

77.5%

2.4

2003

[16] Stress Response Caused by System Response Time when Searching for Information on the Internet M. Trimmel, ..., and S. Haring Human Factors: The Journal of Human Factors and Ergonomics Society 2003 - 49 citations - Show abstract - Cite 77.5% topic match

Investigates stress from system response times (SRT) in internet searches. Shows increased heart rate and stress with longer SRTs, independent of user expertise. Suggests fast Internet connections to reduce stress, highlighting the impact of SRT on user experience.

Investigates stress from system response times (SRT) in internet searches. Shows increased heart rate and stress with longer SRTs, independent of user expertise. Suggests fast Internet connections to reduce stress, highlighting the impact of SRT on user experience.

76.3%

2.1

2007

[17] Optimized Inverted List Assignment in Distributed Search Engine Architectures Jiangong Zhang and Torsten Suel 2007 IEEE International Parallel and Distributed Processing Symposium 2007 - 36 citations - Show abstract - Cite 76.3% topic match

Optimizes index data allocation to reduce search query time. By collocating frequently queried terms, it significantly lessens communication costs. Focuses on distributed search engine efficiency, relevant for systems like Google Scholar.

Optimizes index data allocation to reduce search query time. By collocating frequently queried terms, it significantly lessens communication costs. Focuses on distributed search engine efficiency, relevant for systems like Google Scholar.

68.2%

0.0

2020

[18] Managing tail latency in large scale information retrieval systems J. Mackenzie ACM SIGIR Forum 2020 - 0 citations - Show abstract - Cite Abstract: As both the availability of internet access and the prominence of smart devices continue to increase, data is being generated at a rate faster than ever before. This massive increase in data production comes with many challenges, including efficiency concerns for the storage and retrieval of such large-scale data. However, users have grown to expect the sub-second response times that are common in most modern search engines, creating a problem --- how can such large amounts of data continue to be served efficiently enough to satisfy end users? This dissertation investigates several issues regarding tail latency in large-scale information retrieval systems. Tail latency corresponds to the high percentile latency that is observed from a system --- in the case of search, this latency typically corresponds to how long it takes for a query to be processed. In particular, keeping tail latency as low as possible translates to a good experience for all users, as tail latency is directly related to the worst-case latency and hence, the worst possible user experience. The key idea in targeting tail latency is to move from questions such as "what is the median latency of our search engine?" to questions which more accurately capture user experience such as "how many queries take more than 200ms to return answers?" or "what is the worst case latency that a user may be subject to, and how often might it occur?" While various strategies exist for efficiently processing queries over large textual corpora, prior research has focused almost entirely on improvements to the average processing time or cost of search systems. As a first contribution, we examine some state-of-the-art retrieval algorithms for two popular index organizations, and discuss the trade-offs between them, paying special attention to the notion of tail latency. This research uncovers a number of observations that are subsequently leveraged for improved search efficiency and effectiveness. We then propose and solve a new problem, which involves processing a number of related query variations together, known as multi-queries, to yield higher quality search results. We experiment with a number of algorithmic approaches to efficiently process these multi-queries, and report on the cost, efficiency, and effectiveness trade-offs present with each. Finally, we examine how predictive models can be used to improve the tail latency and end-to-end cost of a commonly used multi-stage retrieval architecture without impacting result effectiveness. By combining ideas from numerous areas of information retrieval, we propose a prediction framework which can be used for training and evaluating several efficiency/effectiveness trade-off parameters, resulting in improved trade-offs between cost, result quality, and tail latency. 68.2% topic match

Analyzes tail latency in information retrieval systems. Focuses on tail latency's impact on user experience, querying processing time. Proposes multi-queries approach for enhancing search efficiency and results quality.

Analyzes tail latency in information retrieval systems. Focuses on tail latency's impact on user experience, querying processing time. Proposes multi-queries approach for enhancing search efficiency and results quality.

67.6%

1.9

2015

[19] WWW=WAIT, WAIT, WAIT: EMOTIONAL REACTIONS TO WAITING ON THE INTERNET Gerard Ryan, ..., and M. Valverde Journal of Electronic Commerce Research 2015 - 17 citations - Show abstract - Cite Abstract: 1. IntroductionThis study examines the topic of waiting on the Internet. Academics, professionals and users have been complaining about the slowness of the Internet since the mid 90's [Chebat et al. 2010; Dabholkar & Sheng 2009; Dellaert & Kahn 1999; Sears et al. 1997; Tze-Hsien & Ching-Jui 2014]. Although the Internet is much faster nowadays, consumers' expectations have also increased significantly. We now expect instantaneous results from the Internet [Kedrosky 2006; Weatherhead 2014]. Just as waiting in everyday life is undesirable, waiting on the Internet produces negative effects on the Internet user experience, which in turn leads to negative word-of-mouth [Roman & Riquelme 2014]. The phenomenon of web rage has been widely reported in the popular press [Team Register 2014], while books and pop songs have been dedicated to the experience of waiting on the Internet [Bowman 2002; Ryan 2014]. It has been suggested that waiting online provokes negative feelings of frustration and impatience on the part of the user [Roman & Riquelme 2014; Eisenberg 2002], which can lead users to abandon web sites [Dabholkar & Sheng 2008; Kaufman-Scarborough & Lindquist 2002; Rajamma et al. 2009] and to develop negative attitudes towards them [Rose & Straub 2001]. Waiting on the Internet has also been linked been linked to a lack of trust in the security of web sites [Yoon 2002], to the interruption of the 'flow' experience [Rettie 2001; Van Beveren et al. 2003] and to problems in maintaining interactivity [Stewart & Pavlou 2002].Most of the existing studies on this topic focus on just one single type of online waiting situation, commonly labelled download delay, i.e. the delay involved in waiting for web pages to download and appear on the user's screen [Nah 2004; Rose & Straub 2001; Rose et al. 2003; Weinberg 2000; Weinberg et al. 2003]. From a marketing perspective this ignores a whole range of online waiting situations in online services that were identified and described by Ryan & Valverde [2005], including waiting for replies from customer support services, waiting for onscreen advertising to end, waiting to access traffic congested websites or for websites temporarily closed for maintenance. The overriding emphasis on measuring just tolerance of download time has left many questions unanswered and many gaps in our knowledge of the wider consumer experience while waiting on the Internet. Indeed, no previous study has examined the emotional or affective consequences of waiting on the Internet in any great detail. This gap in our knowledge and understanding of online consumer behaviour is surprising considering that from a marketing perspective, waiting on the Internet has been linked to serious issues including the loss of potential clients and the elimination of one of the main motivations for shopping online, i.e. timesaving and convenience [Foucault & Scheufele 2002; Rodriguez 2002,]. The objective of this paper is to identify and consider the emotional reactions of consumers when they experience waiting on the Internet.2. Literature reviewThis section examines the literature related to waiting on the Internet. Firstly, we examine the three main disciplines that consider this topic, Human Computer Interaction (HCI), Internet Quality of Service (QoS) and e-Services Marketing. Secondly, we examine the related literature on consumers' reactions to waiting in traditional (offline services) in order to consider the implications for research on waiting in online services. Thirdly, we examine the literature on emotions in order to consider the emotional reaction to waiting on the Internet.In HCI research, the concept of 'delay' has been studied since the early 1980's [Shneiderman 1984; Weiss et al. 1982]. It demonstrates that delays in the response of personal computers cause uncomfortable feelings of annoyance and frustration among users [Schleifer & Amick 1989]. … 67.6% topic match

Investigates emotional reactions to Internet waiting times. Explores user frustration linked to increased expectations of speed. Useful for understanding psychological impacts of search latency on user satisfaction.

Investigates emotional reactions to Internet waiting times. Explores user frustration linked to increased expectations of speed. Useful for understanding psychological impacts of search latency on user satisfaction.

65.3%

0.2

2006

[20] Understanding the Direct and Interaction Effects of Web Delay and Related Factors D. Galletta, ..., and P. Polak Journal Not Provided 2006 - 3 citations - Show abstract - Cite Abstract: The benefits of using the Internet are partially offset by one aspect of its usability: highly variable, intermittent, but frequent inter-page delay. For several years, the HCI literature has studied user reactions to long computer response time in clerical applications, but few studies have examined this problem in the domain of the Web. Examining the delay problem in a Web context is important, because the Web touches many more users, most of whom have little formal computer or task training. Hence, we have examined in our labs consequences of delay, along with factors that interact with delay. Some of our experiments have been published and some are still under review. Consequences of delay that we examined include user attitudes, behavior, and performance. Factors that we examined for possible interactions included site depth, familiarity with terminology used in organizing the site, variability of the delay, and feedback (continuous and gradual filling of the screen to make it obvious that the page is indeed loading). Experiment 1 (n 196) provided seven levels of delay, ranging from zero to twelve seconds (in two-second increments), and discovered that ill effects began as delay exceeded two seconds. Experiment 2 (n 206) again compared reactions to the same levels of delay, but this time with Mexican subjects. It was found that Mexicans were more patient than subjects in the United States. In both studies, the outcomes differed when comparing a familiar site with an unfamiliar site, suggesting that interactions should be examined more formally. Experiment 3 (n 160) introduced two other factors from the HCI literature, and with a 2 2 2 ANOVA, we assessed the interactions between delay, site depth, and familiarity with the terminology in the site. As predicted, we found a significant three-way interaction. Consistent with more traditional literature, we also found strong direct effects. Experiment 4 (n 152) employed another 2 2 2 design, but along with delay we analyzed the effects of variability and feedback as interacting variables. Analysis revealed that page-loading feedback is only important when there are long delays, and variability does not seem to be important in influencing attitudes, behavior, and performance of users. Conclusions from the four studies are that user impatience is high; that the results of delay can differ with culture; and that the variables that interact with delay are familiarity with site terminology, depth of the site, and feedback (in a slow site). Variability does not seem to interact with delay. 65.3% topic match

Investigates effects of Web delay on user attitudes and behavior. Conducted experiments exploring reactions to varying delays from zero to twelve seconds. Relevant as it addresses search latency, indirectly impacting user experience.

Investigates effects of Web delay on user attitudes and behavior. Conducted experiments exploring reactions to varying delays from zero to twelve seconds. Relevant as it addresses search latency, indirectly impacting user experience.

64.6%

8.5

2013

[21] A provider-side view of web search response time Yingying Chen, ..., and Zhi-Li Zhang Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM 2013 - 93 citations - Show abstract - Cite 64.6% topic match

Analyzes Web search response time variations and causes. Demonstrates systemic and anomalous factors influencing search response time (SRT) variability. Focuses on Web search systems, indirectly relevant to academic database latency issues like Google Scholar.

Analyzes Web search response time variations and causes. Demonstrates systemic and anomalous factors influencing search response time (SRT) variability. Focuses on Web search systems, indirectly relevant to academic database latency issues like Google Scholar.

58.1%

0.3

2014

[22] Algorithms and Techniques for Proactive Search Christopher Shaun Wagner, ..., and A. Hurson J. Inf. Process. 2014 - 3 citations - Show abstract - Cite 58.1% topic match

Introduces proactive search to reduce search query time. Addresses search latency by pre-identifying resources using user history and predictive algorithms. Conducts real-world tests, supporting the effectiveness in enhancing user experience by reducing waiting times.

Introduces proactive search to reduce search query time. Addresses search latency by pre-identifying resources using user history and predictive algorithms. Conducts real-world tests, supporting the effectiveness in enhancing user experience by reducing waiting times.

56.8%

1.2

2017

[23] Investigating Users' Time Perception during Web Search Cheng Luo, ..., and Shaoping Ma Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval 2017 - 9 citations - Show abstract - Cite 56.8% topic match

Investigates users' time perception during web search. Explores the discrepancy between perceived time and objective time in search processes. Validates Vierordt's law in web search, impacting design considerations for search latency and user satisfaction.

Investigates users' time perception during web search. Explores the discrepancy between perceived time and objective time in search processes. Validates Vierordt's law in web search, impacting design considerations for search latency and user satisfaction.

56.1%

0.0

2016

[24] Understanding User Adjustment to Slow Search Ryan Burton and Kevyn Collins-Thompson Journal Not Provided 2016 - 0 citations - Show abstract - Cite 56.1% topic match

Analyzes adjustment to slow search on user behavior. Compares user adjustment by search task and exposes behavior change with relevance gains in slow search. Argues slow search's low variance in outcomes as a robust alternative, relevant to search latency impact on user experience.

Analyzes adjustment to slow search on user behavior. Compares user adjustment by search task and exposes behavior change with relevance gains in slow search. Argues slow search's low variance in outcomes as a robust alternative, relevant to search latency impact on user experience.

53.7%

3.3

2021

[25] Adaptation in Information Search and Decision-Making under Time Constraints Anita Crescenzi, ..., and Yuan Li Proceedings of the 2021 Conference on Human Information Interaction and Retrieval 2021 - 11 citations - Show abstract - Cite 53.7% topic match

Investigates the impact of time constraints on information search. Studies how searchers adapt their process and perception under time constraints. May indirectly relate to search query time affecting user experience and usage.

Investigates the impact of time constraints on information search. Studies how searchers adapt their process and perception under time constraints. May indirectly relate to search query time affecting user experience and usage.

52.2%

8.5

2000

[26] System Response Time and User Satisfaction: An Experimental Study of Browser-based Applications John A. Hoxmeier and Christopher A. Dicesare Journal Not Provided 2000 - 210 citations - Show abstract - Cite Abstract: With rapid advances in hardware speed and data communication bandwidth, one might not expect to have to deal with issues such as response time and system performance. But these issues remain a very real concern today. Lengthy system response times may cause lower satisfaction and poor productivity among users. Lowered user satisfaction may lead to discontinued use of an application, especially in discretionary applications such as those found on the Internet. The intent of this experimental research is to (1) substantiate that slow system response time leads to dissatisfaction; (2) assess the point at which users may become dissatisfied with system response time; (3) determine a threshold at which dissatisfaction may lead to discontinued use of the application, and (4) determine if experience influences response time tolerance. The results showed that indeed satisfaction does decrease as response time increases. However, instant response was not perceived as making the system easier to use or learn. It also showed that for discretionary applications, there appears to be a level of intolerance in the 12-second response range. Background and Introduction With rapid advances in hardware speed and data communication bandwidth, one might not expect to have to deal with issues such as response time and system performance. But these issues remain a very real concern today. As systems become more powerful, their applications become more complex as users expect more functionality and richer information. While response time is recognized as a component in some application usability and human factors studies (Goodwin, 1987; Nielsen, 1994; Shneiderman, 1991; Shneiderman, 1998) users today face similar system response time issues that faced users a decade ago. As web developers experience slow response times due to lengthy downloads of graphic intensive material, they are redesigning the sites to minimize this overhead. This will not be an acceptable long-term solution to accommodating electronic commerce. The network may have pockets of high-speed access and adequate bandwidth, but overall high-speed access is unavailable and many of the sites may not have been designed to handle the traffic and data volume 52.2% topic match

Investigates system response time's effect on user satisfaction. Found dissatisfaction increases with longer response times; critical dissatisfaction threshold around 12 seconds. Study is based on browser-based applications, not exclusively search systems like Google Scholar.

Investigates system response time's effect on user satisfaction. Found dissatisfaction increases with longer response times; critical dissatisfaction threshold around 12 seconds. Study is based on browser-based applications, not exclusively search systems like Google Scholar.

49.8%

6.9

2013

[27] Slow Search: Information Retrieval without Time Constraints J. Teevan, ..., and Yubin Kim Symposium on Human-Computer Interaction and Information Retrieval 2013 - 75 citations - Show abstract - Cite 49.8% topic match

Explores the concept of "slow search" in information retrieval. Investigates the trade-off between search speed and quality of search results via log analysis and user surveys. Identifies scenarios where users prioritize result quality over retrieval speed, challenging conventional speed expectations.

Explores the concept of "slow search" in information retrieval. Investigates the trade-off between search speed and quality of search results via log analysis and user surveys. Identifies scenarios where users prioritize result quality over retrieval speed, challenging conventional speed expectations.

49.3%

2.8

2016

[28] The impacts of time constraint on users' search strategy during search process Chang Liu and Yiming Wei Proceedings of the Association for Information Science and Technology 2016 - 22 citations - Show abstract - Cite 49.3% topic match

Examines impact of time constraint on search strategies. Shows time constraints affect initial interaction with search results but not overall session. Relevant to understanding how time factors influence user search behavior, albeit not directly linked to query time effects on user experience in systems like Google Scholar.

Examines impact of time constraint on search strategies. Shows time constraints affect initial interaction with search results but not overall session. Relevant to understanding how time factors influence user search behavior, albeit not directly linked to query time effects on user experience in systems like Google Scholar.

49.0%

0.0

2016

[29] Time-Quality Trade-offs in Search Ryan Burton Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval 2016 - 0 citations - Show abstract - Cite 49.0% topic match

Examines the trade-off between search speed and result quality. Proposes researching user behavior towards slower searches that potentially yield higher quality results. Outlines preliminary studies on user attitudes and behaviors regarding slow search, suggesting further investigation in this area.

Examines the trade-off between search speed and result quality. Proposes researching user behavior towards slower searches that potentially yield higher quality results. Outlines preliminary studies on user attitudes and behaviors regarding slow search, suggesting further investigation in this area.

43.3%

0.1

2005

[30] Disassembling Web Site Response Time K. Renaud and T. Chiew Journal Not Provided 2005 - 2 citations - Show abstract - Cite 43.3% topic match

Provides methods to measure website response times. Discusses impact of response time on user perception, employing varied measurement techniques. This research indirectly touches on user experience via latency impacts; however, it does not focus specifically on systems like Google Scholar.

Provides methods to measure website response times. Discusses impact of response time on user perception, employing varied measurement techniques. This research indirectly touches on user experience via latency impacts; however, it does not focus specifically on systems like Google Scholar.

41.3%

0.8

2016

[31] Manipulating Time Perception of Web Search Users Cheng Luo, ..., and Deli Yang Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval 2016 - 7 citations - Show abstract - Cite 41.3% topic match

Investigates the impact of temporal relevance on time perception. Reveals time perception in web searches can significantly differ from actual time. Suggests user perception of time affects web search studies, relevant for evaluating search query time impacts.

Investigates the impact of temporal relevance on time perception. Reveals time perception in web searches can significantly differ from actual time. Suggests user perception of time affects web search studies, relevant for evaluating search query time impacts.

34.6%

2.6

1997

[32] Internet delay effects: how users perceive quality, organization, and ease of use of information A. Sears, ..., and M. Borella CHI '97 Extended Abstracts on Human Factors in Computing Systems 1997 - 72 citations - Show abstract - Cite 34.6% topic match

Investigates the effects of Internet delays on user perceptions. Shows delays decrease satisfaction in finding and evaluating information. Focuses on general Internet use, not specific to academic systems like Google Scholar.

Investigates the effects of Internet delays on user perceptions. Shows delays decrease satisfaction in finding and evaluating information. Focuses on general Internet use, not specific to academic systems like Google Scholar.

30.0%

0.1

2016

[33] Preliminary exploration of the effect of time constraint on search interactions on webpages Chang Liu and Tao Xu 2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL) 2016 - 1 citations - Show abstract - Cite 30.0% topic match

Investigates the effect of time constraints on web search behaviors. Indicates users under time pressure visit fewer pages and alter interaction patterns. Suggests implications for digital library design regarding users' time limitations.

Investigates the effect of time constraints on web search behaviors. Indicates users under time pressure visit fewer pages and alter interaction patterns. Suggests implications for digital library design regarding users' time limitations.

27.9%

0.4

1982

[34] Computer System Response Time and Psychophysiological Stress II* S. Weiss, ..., and D. J. Martin Proceedings of the Human Factors and Ergonomics Society Annual Meeting 1982 - 18 citations - Show abstract - Cite 27.9% topic match

Investigates System Response Time (SRT) and user satisfaction. Explores SRT impact on twenty participants using a time-shared system. Focuses on psychophysiological stress, not directly on query time and user experience in information retrieval systems.

Investigates System Response Time (SRT) and user satisfaction. Explores SRT impact on twenty participants using a time-shared system. Focuses on psychophysiological stress, not directly on query time and user experience in information retrieval systems.

27.7%

3.9

2000

[35] The World Wide Wait: Effects of Delays on User Performance P. Selvidge, ..., and Gregory T. Bender Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2000 - 93 citations - Show abstract - Cite 27.7% topic match

Investigates effects of web delays on user performance. Measures impact of varied delays (1s, 30s, 60s) on user frustration and task success. Focuses on airline website tasks, not specifically search query times or academic databases.

Investigates effects of web delays on user performance. Measures impact of varied delays (1s, 30s, 60s) on user frustration and task success. Focuses on airline website tasks, not specifically search query times or academic databases.

25.4%

5.5

2010

[36] A refreshing perspective of search engine caching B. B. Cambazoglu, ..., and B. Bridge The Web Conference 2010 - 78 citations - Show abstract - Cite 25.4% topic match

Introduces algorithm optimizing search engine caching. Focuses on maintaining cache freshness to ensure low latency in search results. Discusses hit rate improvements and reduced search result staleness, indirectly impacting user experience by ensuring faster search responses.

Introduces algorithm optimizing search engine caching. Focuses on maintaining cache freshness to ensure low latency in search results. Discusses hit rate improvements and reduced search result staleness, indirectly impacting user experience by ensuring faster search responses.

25.3%

0.0

2013

[37] Query-time optimization techniques for structured queries in information retrieval James Allan and Marc-Allen Cartright https://doi.org/10.7275/QC1P-PD82 2013 - 0 citations - Show abstract - Cite Abstract: The use of information retrieval (IR) systems is evolving towards larger, more complicated queries. Both the IR industrial and research communities have generated significant evidence indicating that in order to continue improving retrieval effectiveness, increases in retrieval model complexity may be unavoidable. From an operational perspective, this translates into an increasing computational cost to generate the final ranked list in response to a query. Therefore we encounter an increasing tension in the trade-off between retrieval effectiveness (quality of result list) and efficiency (the speed at which the list is generated). This tension creates a strong need for optimization techniques to improve the efficiency of ranking with respect to these more complex retrieval models. This thesis presents three new optimization techniques designed to deal with different aspects of structured queries. The first technique involves manipulation of interpolated subqueries, a common structure found across a large number of retrieval models today. We then develop an alternative scoring formulation to make retrieval models more responsive to dynamic pruning techniques. The last technique is delayed execution, which focuses on the class of queries that utilize term dependencies and term conjunction operations. In each case, we empirically show that these optimizations can significantly improve query processing efficiency without negatively impacting retrieval effectiveness. Additionally, we implement these optimizations in the context of a new retrieval system known as Julien. As opposed to implementing these techniques as one-off solutions hard-wired to specific retrieval models, we treat each technique as a ``behavioral'' extension to the original system. This allows us to flexibly stack the modifications to use the optimizations in conjunction, increasing efficiency even further. By focusing on the behaviors of the objects involved in the retrieval process instead of on the details of the retrieval algorithm itself, we can recast these techniques to be applied only when the conditions are appropriate. Finally, the modular design of these components illustrates a system design that allows improvements to be implemented without disturbing the existing retrieval infrastructure. 25.3% topic match

24.5%

0.7

2000

[38] A study of web users' waiting time F. Nah https://doi.org/10.4018/978-1-931777-00-1.CH011 2000 - 17 citations - Show abstract - Cite 24.5% topic match

Investigates acceptable waiting times for WWW users. Evaluates Nielsen’s 15-second waiting time hypothesis, analyzing user patience and tolerance. Focuses on general Web usage, not specifically on systems like Google Scholar.

Investigates acceptable waiting times for WWW users. Evaluates Nielsen’s 15-second waiting time hypothesis, analyzing user patience and tolerance. Focuses on general Web usage, not specifically on systems like Google Scholar.

24.4%

1.5

2016

[39] Prediction and Predictability for Search Query Acceleration Seung-won Hwang, ..., and Seungjin Choi ACM Transactions on the Web (TWEB) 2016 - 12 citations - Show abstract - Cite Abstract: A commercial web search engine shards its index among many servers, and therefore the response time of a search query is dominated by the slowest server that processes the query. Prior approaches target improving responsiveness by reducing the tail latency, or high-percentile response time, of an individual search server. They predict query execution time, and if a query is predicted to be long-running, it runs in parallel; otherwise, it runs sequentially. These approaches are, however, not accurate enough for reducing a high tail latency when responses are aggregated from many servers because this requires each server to reduce a substantially higher tail latency (e.g., the 99.99th percentile), which we call extreme tail latency. To address tighter requirements of extreme tail latency, we propose a new design space for the problem, subsuming existing work and also proposing a new solution space. Existing work makes a prediction using features available at indexing time and focuses on optimizing prediction features for accelerating tail queries. In contrast, we identify “when to predict?” as another key optimization question. This opens up a new solution of delaying a prediction by a short duration to allow many short-running queries to complete without parallelization and, at the same time, to allow the predictor to collect a set of dynamic features using runtime information. This new question expands a solution space in two meaningful ways. First, we see a significant reduction of tail latency by leveraging “dynamic” features collected at runtime that estimate query execution time with higher accuracy. Second, we can ask whether to override prediction when the “predictability” is low. We show that considering predictability accelerates the query by achieving a higher recall. With this prediction, we propose to accelerate the queries that are predicted to be long-running. In our preliminary work, we focused on parallelization as an acceleration scenario. We extend to consider heterogeneous multicore hardware for acceleration. This hardware combines processor cores with different microarchitectures such as energy-efficient little cores and high-performance big cores, and accelerating web search using this hardware has remained an open problem. We evaluate the proposed prediction framework in two scenarios: (1) query parallelization on a multicore processor and (2) query scheduling on a heterogeneous processor. Our extensive evaluation results show that, for both scenarios of query acceleration using parallelization and heterogeneous cores, the proposed framework is effective in reducing the extreme tail latency compared to a start-of-the-art predictor because of its higher recall, and it improves server throughput by more than 70% because of its improved precision. 24.4% topic match

Investigates accelerating search query times in web search engines. Proposes new strategies to address extreme tail latency across multiple servers. Focuses on system-level optimizations rather than user experience directly.

Investigates accelerating search query times in web search engines. Proposes new strategies to address extreme tail latency across multiple servers. Focuses on system-level optimizations rather than user experience directly.

23.7%

0.2

2020

[40] The Effect of Queries and Search Result Quality on the Rate of Query Abandonment in Interactive Information Retrieval Mustafa Abualsaud Proceedings of the 2020 Conference on Human Information Interaction and Retrieval 2020 - 1 citations - Show abstract - Cite 23.7% topic match

Examines query abandonment related to search result quality. Studies user behavior in relation to the quality of search results and query abandonment rates. Utilizes eye-tracking and questionnaires to analyze user engagement and decision to abandon queries.

Examines query abandonment related to search result quality. Studies user behavior in relation to the quality of search results and query abandonment rates. Utilizes eye-tracking and questionnaires to analyze user engagement and decision to abandon queries.

23.1%

1.1

2021

[41] AutoSens: inferring latency sensitivity of user activity through natural experiments Parth Thakkar, ..., and V. Padmanabhan Proceedings of the 21st ACM Internet Measurement Conference 2021 - 3 citations - Show abstract - Cite 23.1% topic match

23.0%

3.0

2013

[42] When Filling the Wait Makes it Feel Longer: A Paradigm Shift Perspective for Managing Online Delay Weiyin Hong, ..., and Andrew M. Hardin MIS Q. 2013 - 34 citations - Show abstract - Cite 23.0% topic match

Examines management of wait time perceptions in e-commerce. Uses time perception theories to investigate effects of actual wait times on user perceptions. Conducts empirical studies through an experimental travel website, not specifically Google Scholar or similar systems.

Examines management of wait time perceptions in e-commerce. Uses time perception theories to investigate effects of actual wait times on user perceptions. Conducts empirical studies through an experimental travel website, not specifically Google Scholar or similar systems.

22.4%

0.2

2013

[43] ADAPTING WEB CONTENT BASED ON USER CONNECTION SPEED TO ENSURE RESPONSE TIME R. Rostami and T. Chiew Malaysian Journal of Computer Science 2013 - 2 citations - Show abstract - Cite 22.4% topic match

Explores web response time based on user connection speed. Introduces a content adaptation technique to maintain response times within user satisfaction thresholds. Focuses on network variables and adaptable content delivery rather than search query times specifically.

Explores web response time based on user connection speed. Introduces a content adaptation technique to maintain response times within user satisfaction thresholds. Focuses on network variables and adaptable content delivery rather than search query times specifically.

22.0%

0.0

1982

[44] Learning effectiveness: The impact of response time (abstract only) Sherry Weinberg ACM Sigsoc Bulletin 1982 - 1 citations - Show abstract - Cite 22.0% topic match

21.6%

0.0

2013

[45] Directly " Evaluating " the " Cognitive " Impact " of " Search " User " Interfaces : " a " TwoF Pronged " Approach " with " fNIRS Alistair Moffat, ..., and Stefan Haun Journal Not Provided 2013 - 0 citations - Show abstract - Cite 21.6% topic match

Investigates user behavior in degraded search interfaces. Shows unchanged user behavior despite poor search result quality. Focuses on user response to search quality, not query time effect.

Investigates user behavior in degraded search interfaces. Shows unchanged user behavior despite poor search result quality. Focuses on user response to search quality, not query time effect.

21.3%

0.7

1990

[46] The Effect of System Response Times on Temporal Predictability of Work Flow in Human-Computer Interaction F. Schaefer Human Performance 1990 - 23 citations - Show abstract - Cite 21.3% topic match

Investigates impact of system response times on user predictability. Shows longer response times worsen users' ability to anticipate next steps. Focuses on psychophysiological stress and temporal sensitivity in human-computer interaction.

Investigates impact of system response times on user predictability. Shows longer response times worsen users' ability to anticipate next steps. Focuses on psychophysiological stress and temporal sensitivity in human-computer interaction.

21.2%

0.0

2015

[47] Time Pressure in Information Search Anita Crescenzi Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval 2015 - 0 citations - Show abstract - Cite 21.2% topic match

Explores impact of time pressure on search behaviors. Examines how perceived time constraints affect user perceptions and experiences during searches. Focuses more on psychological impact of time rather than search query speed.

Explores impact of time pressure on search behaviors. Examines how perceived time constraints affect user perceptions and experiences during searches. Focuses more on psychological impact of time rather than search query speed.

20.6%

0.7

2005

[48] Predicting Query Performance in Intranet Search ∗ Craig Macdonald and U. K. craigm Journal Not Provided 2005 - 13 citations - Show abstract - Cite 20.6% topic match

Investigates query performance prediction in intranet search. Utilizes logged queries and evaluates using mean average precision. Focuses mainly on predictive aspects, less on user experience implications.

Investigates query performance prediction in intranet search. Utilizes logged queries and evaluates using mean average precision. Focuses mainly on predictive aspects, less on user experience implications.

19.1%

0.0

2020

[49] Personalised Web search and user satisfaction: a user-centred evaluation Sara Salehi, ..., and H. Ashman Inf. Res. 2020 - 0 citations - Show abstract - Cite 19.1% topic match

Explores user satisfaction in personalized versus non-personalized search. Personalized search reduced search time and clicks needed to find information. Does not specifically focus on query time's impact on overall user experience.

Explores user satisfaction in personalized versus non-personalized search. Personalized search reduced search time and clicks needed to find information. Does not specifically focus on query time's impact on overall user experience.

18.8%

0.5

2018

[50] Personalizing Information Retrieval Using Search Behaviors and Time Constraints Chang Liu, ..., and Zengwang Yan Proceedings of the 2018 Conference on Human Information Interaction & Retrieval 2018 - 3 citations - Show abstract - Cite 18.8% topic match

Examines impact of time constraints on search behaviors. Investigates how these constraints influence users' document usefulness judgments during searches. Does not directly study search query time or system usage effects.

Examines impact of time constraints on search behaviors. Investigates how these constraints influence users' document usefulness judgments during searches. Does not directly study search query time or system usage effects.

18.8%

1.2

1978

[51] The effect of System Response Time on interactive computer aided problem solving Thomas J. Goodman and R. Spence Proceedings of the 5th annual conference on Computer graphics and interactive techniques 1978 - 57 citations - Show abstract - Cite 18.8% topic match

Investigates System Response Time on problem-solving efficiency. Comparatively assesses impact through SRTs of 0.16, 0.72, and 1.49 seconds. Highlights significant performance decline at a higher SRT, applicable to user experience in search systems.

Investigates System Response Time on problem-solving efficiency. Comparatively assesses impact through SRTs of 0.16, 0.72, and 1.49 seconds. Highlights significant performance decline at a higher SRT, applicable to user experience in search systems.

18.5%

0.4

2016

[52] ERWIN - enabling the reproducible investigation of waiting times for arbitrary workflows T. Zinner, ..., and O. Hohlfeld 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX) 2016 - 3 citations - Show abstract - Cite 18.5% topic match

Introduces ERWIN, a tool for reproducible delay assessments. ERWIN assesses delay effects across various applications, enhancing delay study accessibility. Focuses on methodological development rather than direct effects on user experience or system usage.

Introduces ERWIN, a tool for reproducible delay assessments. ERWIN assesses delay effects across various applications, enhancing delay study accessibility. Focuses on methodological development rather than direct effects on user experience or system usage.

17.4%

0.6

1997

[53] The effects of Internet latency on user perception of information content M. Borella, ..., and J. Jacko GLOBECOM 97. IEEE Global Telecommunications Conference. Conference Record 1997 - 16 citations - Show abstract - Cite 17.4% topic match

17.0%

1.3

2017

[54] Adaptive Persistence for Search Effectiveness Measures Jiepu Jiang and James Allan Proceedings of the 2017 ACM on Conference on Information and Knowledge Management 2017 - 9 citations - Show abstract - Cite 17.0% topic match

Introduces adaptive persistence in search effectiveness measures. Models user browsing behavior changes based on search result relevancy. Correlates adjusted user behavior with actual user satisfaction ratings.

Introduces adaptive persistence in search effectiveness measures. Models user browsing behavior changes based on search result relevancy. Correlates adjusted user behavior with actual user satisfaction ratings.

16.6%

0.0

2013

[55] Human Factors – rok 2013 , roč . 55 Číslo 5 ( October 2013 ) Attentional Processes Florian Weber, ..., and A. Luscher Journal Not Provided 2013 - 0 citations - Show abstract - Cite 16.6% topic match

Investigates the effect of system response time on user experience. Shows how varying delay lengths affect user performance and satisfaction. Focuses on sequential task simulations, not search query systems specifically.

Investigates the effect of system response time on user experience. Shows how varying delay lengths affect user performance and satisfaction. Focuses on sequential task simulations, not search query systems specifically.

16.5%

0.2

2019

[56] Google Scholar Through the Eyes of Academics Esadiye Pekdemir Journal Not Provided 2019 - 1 citations - Show abstract - Cite 16.5% topic match

Explores academics' views on Google Scholar. Investigates usage, benefits, and issues faced by academics in Google Scholar. Does not specifically address search query time or its impact on user experience.

Explores academics' views on Google Scholar. Investigates usage, benefits, and issues faced by academics in Google Scholar. Does not specifically address search query time or its impact on user experience.

15.8%

0.6

2013

[57] Fading Away: Dilution and User Behaviour Paul Thomas, ..., and Alistair Moffat European Workshop on Human-Computer Interaction and Information Retrieval 2013 - 7 citations - Show abstract - Cite 15.8% topic match

Investigates user behavior in response to poor search results. Compares user actions between standard and deliberately degraded search systems. Explores impact on query reformulation and search completion time but does not focus on search query time effects.

Investigates user behavior in response to poor search results. Compares user actions between standard and deliberately degraded search systems. Explores impact on query reformulation and search completion time but does not focus on search query time effects.

15.7%

0.7

2019

[58] How do Computer Scientists Use Google Scholar?: A Survey of User Interest in Elements on SERPs and Author Profile Pages Jaewon Kim, ..., and W. Bruce Croft Journal Not Provided 2019 - 4 citations - Show abstract - Cite 15.7% topic match

Investigates user interest in Google Scholar's SERPs and APPs. Focuses on how query intent and research area familiarity influence user interest. Does not explicitly explore the impact of search query time on user experience or usage.

Investigates user interest in Google Scholar's SERPs and APPs. Focuses on how query intent and research area familiarity influence user interest. Does not explicitly explore the impact of search query time on user experience or usage.

15.6%

2.0

2016

[59] FOCUS: Shedding light on the high search response time in the wild Dapeng Liu, ..., and Dai Tan IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications 2016 - 17 citations - Show abstract - Cite 15.6% topic match

15.4%

0.1

2007

[60] The Influence of System Response Time on Human-information System Interaction Efficiency Ding Jing Chinese Journal of Ergonomics 2007 - 1 citations - Show abstract - Cite 15.4% topic match

15.1%

0.2

2003

[61] Analysing the latency of World Wide Web applications A. Ruddle, ..., and R. Nicoll Software: Practice and Experience 2003 - 4 citations - Show abstract - Cite 15.1% topic match

Analyzes factors contributing to Web application latency. Develops a structured timing model to break down delay components. Focuses on quality of service in distributed learning environments, not specifically on search systems like Google Scholar.

Analyzes factors contributing to Web application latency. Develops a structured timing model to break down delay components. Focuses on quality of service in distributed learning environments, not specifically on search systems like Google Scholar.

15.1%

1.3

2000

[62] Understanding the Relation Between Network Quality of Service and the Usability of Distributed Multimedia Documents A. Sears and J. Jacko Human–Computer Interaction 2000 - 32 citations - Show abstract - Cite 15.1% topic match

15.0%

1.2

2016

[63] Search Result Prefetching Using Cursor Movement Fernando Diaz, ..., and Ryen W. White Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval 2016 - 10 citations - Show abstract - Cite 15.0% topic match

15.0%

0.2

1984

[64] Computer response time and user performance during data entry T. W. Butler AT&T Bell Laboratories Technical Journal 1984 - 8 citations - Show abstract - Cite 15.0% topic match

14.9%

1.7

1995

[65] Standardized task strain and system response times in human-computer interaction. M. Thum, ..., and W. Ray Ergonomics 1995 - 48 citations - Show abstract - Cite 14.9% topic match

Examines system response times (SRTs) effects on user stress. Studies stress impacts from varying SRTs of 0.5, 1.5, and 4.5 seconds in standardized tasks. Measured physiological responses, emotional states, and task performance due to SRT changes.

Examines system response times (SRTs) effects on user stress. Studies stress impacts from varying SRTs of 0.5, 1.5, and 4.5 seconds in standardized tasks. Measured physiological responses, emotional states, and task performance due to SRT changes.

14.2%

1.1

2021

[66] Subjectively Experienced Time and User Satisfaction: An Experimental Study of Progress Indicator Design in Mobile Application Sara Willermark, ..., and Hannah Pehrson Hawaii International Conference on System Sciences 2021 - 4 citations - Show abstract - Cite 14.2% topic match

14.1%

1.6

2011

[67] Personalized search: Integrating collaboration and social networks Bracha Shapira and Boaz Zabar J. Assoc. Inf. Sci. Technol. 2011 - 22 citations - Show abstract - Cite 14.1% topic match

Reviews methods for personalizing search results. Examines impact of integrating recommender systems on improving search relevancy. Does not specifically address search query time or user experience metrics.

Reviews methods for personalizing search results. Examines impact of integrating recommender systems on improving search relevancy. Does not specifically address search query time or user experience metrics.

14.0%

9.6

2006

[68] When the Wait Isn't So Bad: The Interacting Effects of Website Delay, Familiarity, and Breadth D. Galletta, ..., and P. Polak Inf. Syst. Res. 2006 - 176 citations - Show abstract - Cite 14.0% topic match

Examines interaction effects of delay on user experience. Studies delay, site breadth, and content familiarity affecting user performance and attitudes. Conducted with user experiments, focusing on navigation and information retrieval tasks.

Examines interaction effects of delay on user experience. Studies delay, site breadth, and content familiarity affecting user performance and attitudes. Conducted with user experiments, focusing on navigation and information retrieval tasks.

13.9%

3.6

2001

[69] Measuring Client-Perceived Response Time on the WWW R. Rajamony and Mootaz Elnozahy USENIX Symposium on Internet Technologies and Systems 2001 - 85 citations - Show abstract - Cite 13.9% topic match

13.5%

0.0

2015

[70] Slow Search: Improving Information Retrieval Using Human Assistance J. Teevan Proceedings of the 24th ACM International on Conference on Information and Knowledge Management 2015 - 0 citations - Show abstract - Cite 13.5% topic match

Proposes the "slow search" concept to improve search quality. Utilizes additional time for human-assisted search, enhancing result relevance and user experience. Contrasts conventional fast search expectations, focusing on quality over speed.

Proposes the "slow search" concept to improve search quality. Utilizes additional time for human-assisted search, enhancing result relevance and user experience. Contrasts conventional fast search expectations, focusing on quality over speed.

13.1%

8.1

2014

[71] Predictive parallelization: taming tail latencies in web search Myeongjae Jeon, ..., and S. Rixner Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval 2014 - 82 citations - Show abstract - Cite Abstract: Web search engines are optimized to reduce the high-percentile response time to consistently provide fast responses to almost all user queries. This is a challenging task because the query workload exhibits large variability, consisting of many short-running queries and a few long-running queries that significantly impact the high-percentile response time. With modern multicore servers, parallelizing the processing of an individual query is a promising solution to reduce query execution time, but it gives limited benefits compared to sequential execution since most queries see little or no speedup when parallelized. The root of this problem is that short-running queries, which dominate the workload, do not benefit from parallelization. They incur a large parallelization overhead, taking scarce resources from long-running queries. On the other hand, parallelization substantially reduces the execution time of long-running queries with low overhead and high parallelization efficiency. Motivated by these observations, we propose a predictive parallelization framework with two parts: (1) predicting long-running queries, and (2) selectively parallelizing them. For the first part, prediction should be accurate and efficient. For accuracy, we study a comprehensive feature set covering both term features (reflecting dynamic pruning efficiency) and query features (reflecting query complexity). For efficiency, to keep overhead low, we avoid expensive features that have excessive requirements such as large memory footprints. For the second part, we use the predicted query execution time to parallelize long-running queries and process short-running queries sequentially. We implement and evaluate the predictive parallelization framework in Microsoft Bing search. Our measurements show that under moderate to heavy load, the predictive strategy reduces the 99th-percentile response time by 50% (from 200 ms to 100 ms) compared with prior approaches that parallelize all queries. 13.1% topic match

Investigates tail latencies in web search optimization. Explores the limited benefit of parallelizing the processing of short-running queries. Focuses on high-percentile response time reduction, not directly on user experience or usage metrics.

Investigates tail latencies in web search optimization. Explores the limited benefit of parallelizing the processing of short-running queries. Focuses on high-percentile response time reduction, not directly on user experience or usage metrics.

12.9%

0.1

2014

[72] Too little time?: time constraints and time pressure in information search Anita Crescenzi Proceedings of the 5th Information Interaction in Context Symposium 2014 - 1 citations - Show abstract - Cite 12.9% topic match

12.8%

2.1

1987

[73] Experimental investigation of psychophysiological stress-reactions induced by different system response times in human-computer interaction. W. Kuhmann, ..., and Johanna Alexander Ergonomics 1987 - 77 citations - Show abstract - Cite 12.8% topic match

Investigates effects of system response times on stress. Examined both long and short system response times, measuring physiological reactions and performance. Focuses on human-computer interaction, primarily in a lab setting with simulated tasks.

Investigates effects of system response times on stress. Examined both long and short system response times, measuring physiological reactions and performance. Focuses on human-computer interaction, primarily in a lab setting with simulated tasks.

12.5%

0.0

2021

[74] Balancing Latency and Quality in Web Search Liang Zhou and K. Ramakrishnan 2021 IEEE International Conference on Networking, Architecture and Storage (NAS) 2021 - 0 citations - Show abstract - Cite 12.5% topic match

12.3%

0.0

1987

[75] The relationship between system response time, working memory, and task complexity: An empirical investigation E. Schultz, ..., and A. M. Alvare Journal Not Provided 1987 - 0 citations - Show abstract - Cite 12.3% topic match

12.3%

1.1

2014

[76] Analytical Performance Modeling for Top-K Query Processing Hao Wu and Hui Fang Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management 2014 - 11 citations - Show abstract - Cite 12.3% topic match

Develops a model to predict top-K query processing time. Focuses on enhancing online query scheduling and load balancing through performance prediction. May indirectly relate to user experience by reducing waiting times.

Develops a model to predict top-K query processing time. Focuses on enhancing online query scheduling and load balancing through performance prediction. May indirectly relate to user experience by reducing waiting times.

12.0%

0.6

2019

[77] Comprehensive Characterization of an Open Source Document Search Engine Zacharias Hadjilambrou, ..., and Yiannakis Sazeides ACM Transactions on Architecture and Code Optimization (TACO) 2019 - 3 citations - Show abstract - Cite 12.0% topic match

Investigates factors influencing response time in search engines. Analyzes intra-server index partitioning, which affects response times and throughput. Focus on technical performance aspects, not directly on user experience or usage patterns.

Investigates factors influencing response time in search engines. Analyzes intra-server index partitioning, which affects response times and throughput. Focus on technical performance aspects, not directly on user experience or usage patterns.

11.8%

0.2

2006

[78] Efficient query processing in large web search engines Torsten Suel and Xiaohui Long Journal Not Provided 2006 - 4 citations - Show abstract - Cite 11.8% topic match

Investigates efficiency techniques in web search engine queries. Explores index compression, caching, and query pruning to enhance query throughput. Focuses on general search engine optimization but does not specifically address user experience or usage impacts relating to query time.

Investigates efficiency techniques in web search engine queries. Explores index compression, caching, and query pruning to enhance query throughput. Focuses on general search engine optimization but does not specifically address user experience or usage impacts relating to query time.

11.5%

0.2

2012

[79] Model for Predicting End User Web Page Response Time Sathya Narayanan Nagarajan and Srijith Ravikumar ArXiv 2012 - 3 citations - Show abstract - Cite - PDF 11.5% topic match

11.4%

6.7

2012

[80] Learning to predict response times for online query scheduling Craig Macdonald, ..., and I. Ounis Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2012 - 80 citations - Show abstract - Cite 11.4% topic match

11.3%

0.0

2021

[81] User’s Perception of Delay: An Experiment Using Progress Indicators Chatpong Tangmanee and Surached Wisansirikul 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) 2021 - 0 citations - Show abstract - Cite 11.3% topic match

11.2%

0.1

2004

[82] The effect of system response time on visual search in Web pages P. Schaik and Jonathan Ling Electron. Libr. 2004 - 3 citations - Show abstract - Cite 11.2% topic match

Investigates the impact of system response time on Web usability. Assesses how varying response times affect user performance and subjective measures. Focuses on choice reaction tasks using text displays on web pages.

Investigates the impact of system response time on Web usability. Assesses how varying response times affect user performance and subjective measures. Focuses on choice reaction tasks using text displays on web pages.

11.1%

1.3

2013

[83] Improving the Human–Computer Dialogue With Increased Temporal Predictability Florian Weber, ..., and R. Thomaschke Human Factors: The Journal of Human Factors and Ergonomics Society 2013 - 15 citations - Show abstract - Cite 11.1% topic match

11.1%

1.0

2013

[84] Taking Our Sweet Time to Search M. Dörk, ..., and Rosamund Davies Journal Not Provided 2013 - 12 citations - Show abstract - Cite 11.1% topic match

Advocates for the value of "slow search." Proposes design principles for intentionally slower search processes. Contrasts rapid search results with deeper, more engaging search experiences.

Advocates for the value of "slow search." Proposes design principles for intentionally slower search processes. Contrasts rapid search results with deeper, more engaging search experiences.

11.0%

0.6

2021

[85] Time Costs and Search Behavior Y. Hsiao, ..., and Le Zhang ERN: Search 2021 - 2 citations - Show abstract - Cite 11.0% topic match

Investigates effects of time cost on search behavior. Highlights that time cost affects search extent but less than monetary cost. Does not specifically analyze systems like Google Scholar or query time effects on user experience.

Investigates effects of time cost on search behavior. Highlights that time cost affects search extent but less than monetary cost. Does not specifically analyze systems like Google Scholar or query time effects on user experience.

10.8%

1.3

2012

[86] Online result cache invalidation for real-time web search Xiao Bai and F. Junqueira Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2012 - 16 citations - Show abstract - Cite 10.8% topic match

10.3%

32.4

2014

[87] The Effects of Interactive Latency on Exploratory Visual Analysis Zhicheng Liu and Jeffrey Heer IEEE Transactions on Visualization and Computer Graphics 2014 - 315 citations - Show abstract - Cite 10.3% topic match

Investigates impact of interactive latency on exploratory visual analysis. Finds that a 500ms delay significantly reduces user interaction and effectiveness. Although focused on visualizations, findings may generalize to query systems like Google Scholar.

Investigates impact of interactive latency on exploratory visual analysis. Finds that a 500ms delay significantly reduces user interaction and effectiveness. Although focused on visualizations, findings may generalize to query systems like Google Scholar.

10.1%

0.8

2003

[88] The effect of early packet loss on Web page download times James Hall, ..., and A. Moore Journal Not Provided 2003 - 17 citations - Show abstract - Cite 10.1% topic match

10.0%

1.1

2010

[89] Comparative analysis of some search engines Joseph Edosomwan and Taiwo O. Edosomwan South African Journal of Science 2010 - 15 citations - Show abstract - Cite 10.0% topic match

Provides a comparative analysis of response times among search engines. Shows Google had the best average response time at 2 seconds. Focuses on precision and response time, not explicitly on user experience.

Provides a comparative analysis of response times among search engines. Shows Google had the best average response time at 2 seconds. Focuses on precision and response time, not explicitly on user experience.

9.9%

1.8

2014

[90] Delayed system response times affect immediate physiology and the dynamics of subsequent button press behavior. C. Kohrs, ..., and A. Brechmann Psychophysiology 2014 - 18 citations - Show abstract - Cite 9.9% topic match

9.8%

0.1

1995

[91] Effects of time stress on psychophysiological responses during data entry tasks. H. Harada, ..., and Y. Kikuchi Applied human science : journal of physiological anthropology 1995 - 2 citations - Show abstract - Cite 9.8% topic match

Investigates physio-psychological impact of system response times. Slow system response increased emotional irritation and operational errors. Focuses on data entry context, not specifically search systems like Google Scholar.

Investigates physio-psychological impact of system response times. Slow system response increased emotional irritation and operational errors. Focuses on data entry context, not specifically search systems like Google Scholar.

9.8%

0.6

1981

[92] The Effect of Computer System Response Time Variability on Interactive Graphical Problem Solving Thomas J. Goodman and R. Spence IEEE Transactions on Systems, Man, and Cybernetics 1981 - 24 citations - Show abstract - Cite 9.8% topic match

9.8%

0.1

2013

[93] Search Costs in Consumer Product Choice: Does Delaying the Provision of Information Increase Choice Efficiency? Axel Sonntag ERN: Search 2013 - 1 citations - Show abstract - Cite 9.8% topic match

9.7%

2.1

2007

[94] Putting Google Scholar to the test: a preliminary study Mary L. Robinson and Judith Wusteman Program 2007 - 37 citations - Show abstract - Cite 9.7% topic match

Compares retrieval efficiency of Google Scholar to other engines. Indicates Google Scholar excels in precision and top-page retrieval for scientific queries. Does not directly address search query time's impact on user experience.

Compares retrieval efficiency of Google Scholar to other engines. Indicates Google Scholar excels in precision and top-page retrieval for scientific queries. Does not directly address search query time's impact on user experience.

9.6%

0.2

2012

[95] Improving Response Time and Throughput of Search Engine with Web Caching N. PushpaC., ..., and M. PatnaikL International journal of engineering research and technology 2012 - 3 citations - Show abstract - Cite 9.6% topic match

9.6%

1.1

2003

[96] Examining Tolerance for Online Delays B. Chaparro and P. Selvidge Journal Not Provided 2003 - 24 citations - Show abstract - Cite Abstract: The World Wide Web (WWW) has become an increasingly important medium for communication,commerce, and entertainment. The WWW user population is estimated to grow to 320 million by theyear 2002 (Bouch et al., 2000). The 10th WWW User Survey reported that speed, or taking too long todownload pages, was the third major problem with the Internet reported by users (GVU, 1998). Thisproblem translates into lost profits for e-commerce websites due to effects on users� perceptions andperformance. ZDNet (2002) reported a study of 12,000 online customers that revealed 48% of themgave up trying to purchase products because the web pages took too long to download. Brynijolfssonand Smith (2000) pointed out that trust is probably the single most important factor in e-commerce andthe effects of time delay can significantly reduce consumer trust towards an e-commerce retailer.Unfortunately, addressing the factors that affect download time is not as simple as making hardwareimprovements. Since problems with delays are expected to persist, the effects of delays on aspects ofwebsite usability remain an important concern and are the focus of this study.In this study, user tolerance for delays for three common WWW tasks, including information retrieval, purchasing, and downloading a text file was examined. These tasks were selected since the file sizes differ significantly among the three tasks, so it was expected that tolerance may be higher for those tasks with smaller file sizes. For example, tolerance for delays would be higher for downloading a text file than purchasing or information retrieval, since users may have an expectation that the task should take longer so they may be more willing to wait. End-users may be more tolerant of delays while purchasing online, since information must be exchanged to process the transaction, whereas information retrieval does not require information exchange. The general purpose of this study was to examine how long participants would wait for web pages to load before abandoning a website. Previous research has primarily examined the effect of delays on preference and subjective evaluations of websites, but has failed to focus on the most important aspect,which is end-user behavior. The important question for e-commerce organizations is when will the potential customer leave the website because the wait is too long. The effects of different demographiccharacteristics, such as age (young adults/older adults), Internet experience level (novice/experienced),and type of Internet connection frequently used on tolerance for delays relating to three types of common Internet tasks (information retrieval, purchasing, downloading a text file) were investigated. It was hypothesized that (i) older adults would be more tolerant of delays than younger adults, (ii) participants with more Internet experience would be less tolerant of delays than novice users, (iii) participants would be more tolerant of delays for tasks involving downloading text files than purchasing or information retrieval tasks, and (iv) participants that frequently use high-speed connections to the Internet, e.g. cable or DSL modems, would be less tolerant of delays. 9.6% topic match

9.5%

0.1

1997

[97] The Effect of Internet Delay on the Perceived Quality of Information A. Sears, ..., and M. Borella Interacción 1997 - 2 citations - Show abstract - Cite 9.5% topic match

Investigates Internet delay's impact on perceived information quality. Empirically examines how network latency influences user perceptions of usability. Utilizes analyses of covariance to link delay length with usability perceptions.

Investigates Internet delay's impact on perceived information quality. Empirically examines how network latency influences user perceptions of usability. Utilizes analyses of covariance to link delay length with usability perceptions.

9.4%

0.3

1996

[98] Assessing the impact of time on user interface design Chris W. Johnson and P. Gray ACM SIGCHI Bull. 1996 - 9 citations - Show abstract - Cite 9.4% topic match

9.3%

0.8

2017

[99] Does Document Relevance Affect the Searcher's Perception of Time? Cheng Luo, ..., and Shaoping Ma Proceedings of the Tenth ACM International Conference on Web Search and Data Mining 2017 - 6 citations - Show abstract - Cite 9.3% topic match

9.3%

0.1

2016

[100] User Adaptability to System Delay Brian D. Hall Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems 2016 - 1 citations - Show abstract - Cite 9.3% topic match

9.2%

0.5

2001

[101] Task structure and the apparent duration of hierarchical search N. Tractinsky and Joachim Meyer Int. J. Hum. Comput. Stud. 2001 - 12 citations - Show abstract - Cite 9.2% topic match

9.2%

0.0

2013

[102] Probabilistic search service based on user history on cloud Nandha Gopala Krishnan and Yinoth Kumar 2013 Fifth International Conference on Advanced Computing (ICoAC) 2013 - 0 citations - Show abstract - Cite 9.2% topic match

9.1%

2.3

2020

[103] Countdown Timer Speed M. Ghafurian, ..., and F. Ritter ACM Transactions on Computer-Human Interaction (TOCHI) 2020 - 10 citations - Show abstract - Cite 9.1% topic match

Investigates delay impacts on user decision-making and satisfaction. Explores manipulation of countdown speeds to change perceived delay. Focuses on user-interface design, not specifically search systems.

Investigates delay impacts on user decision-making and satisfaction. Explores manipulation of countdown speeds to change perceived delay. Focuses on user-interface design, not specifically search systems.

8.9%

3.4

2000

[104] The effect of network delay and media on user perceptions of web resources J. Jacko, ..., and M. Borella Behaviour & Information Technology 2000 - 83 citations - Show abstract - Cite 8.9% topic match

8.7%

2.6

2021

[105] Impact of Response Latency on User Behaviour in Mobile Web Search Ioannis Arapakis, ..., and M. Pielot Proceedings of the 2021 Conference on Human Information Interaction and Retrieval 2021 - 9 citations - Show abstract - Cite - PDF 8.7% topic match

8.7%

0.9

2011

[106] Web Server Performance Optimization using Prediction Prefetching Engine Silky Makker, ..., and J. Pandey International Journal of Computer Applications 2011 - 12 citations - Show abstract - Cite 8.7% topic match

8.6%

0.0

2007

[107] User Experience und Zeitschätzung Herbert A. Meyer Journal Not Provided 2007 - 0 citations - Show abstract - Cite 8.6% topic match

8.6%

0.0

1999

[108] The Impact of System Response Time on Navigating through Hypertext Herbert A. Meyer and Michael Hildebrandt Journal Not Provided 1999 - 1 citations - Show abstract - Cite 8.6% topic match

8.5%

0.2

2012

[109] Improving Search Experience on Distributed Leisure Events Richard Schaller, ..., and David Elsweiler European Workshop on Human-Computer Interaction and Information Retrieval 2012 - 3 citations - Show abstract - Cite 8.5% topic match

Examines influence of system modifications on user search experience. Focuses on improved system functionality following observed user difficulties and errors. Results derive from a log-based study involving 860 participants.

Examines influence of system modifications on user search experience. Focuses on improved system functionality following observed user difficulties and errors. Results derive from a log-based study involving 860 participants.

8.5%

0.1

2007

[110] Speeding Up QA: An Index Structure for Question Queries Maidong Hu and A. Elamy 2007 Canadian Conference on Electrical and Computer Engineering 2007 - 2 citations - Show abstract - Cite 8.5% topic match

Proposes a new index structure to improve QA system speed. Aims to reduce response and search times in QA systems using an improved inverted index. Focuses on technical enhancements rather than user experience or usage metrics directly.

Proposes a new index structure to improve QA system speed. Aims to reduce response and search times in QA systems using an improved inverted index. Focuses on technical enhancements rather than user experience or usage metrics directly.

8.4%

0.5

2015

[111] The impact of filler interface on online users' perceived waiting time Hanyang Luo, ..., and D. Zeng 2015 12th International Conference on Service Systems and Service Management (ICSSSM) 2015 - 5 citations - Show abstract - Cite 8.4% topic match

8.3%

0.8

2008

[112] The Triumph of Expediency: The Impact of Google Scholar on Library Instruction D. Ettinger Journal of Library Administration 2008 - 13 citations - Show abstract - Cite 8.3% topic match

Assesses Google Scholar's impact on library instruction. Addresses Google Scholar's usability and acceptance in academia, comparing to traditional databases. Does not specifically investigate search query time or its direct effects on user experience.

Assesses Google Scholar's impact on library instruction. Addresses Google Scholar's usability and acceptance in academia, comparing to traditional databases. Does not specifically investigate search query time or its direct effects on user experience.

8.3%

0.1

2007

[113] Consumers' allocation of cognitive resources in web-based search: an exploratory study Valerie J. Trifts and Elaine Toms Int. J. Electron. Bus. 2007 - 2 citations - Show abstract - Cite 8.3% topic match

8.1%

1.1

2002

[114] Effect of Auditory Waiting Cues on Time Estimation in Speech Recognition Telephony Applications M. Polkosky and James R. Lewis International Journal of Human–Computer Interaction 2002 - 23 citations - Show abstract - Cite 8.1% topic match

8.0%

0.1

1981

[115] Learning effectiveness: The impact of response time (abstract only) S. Weinberg International Conference on Human Factors in Computing Systems 1981 - 6 citations - Show abstract - Cite 8.0% topic match

8.0%

6.4

2008

[116] User adaptation: good results from poor systems Catherine L. Smith and P. Kantor Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008 - 103 citations - Show abstract - Cite 8.0% topic match

7.9%

0.4

2010

[117] User Web Search Behaviour Murtuza Kutub, ..., and Dr Mangesh Bedekar 2010 3rd International Conference on Emerging Trends in Engineering and Technology 2010 - 6 citations - Show abstract - Cite 7.9% topic match

7.7%

1.1

2017

[118] Search Result Prefetching on Desktop and Mobile Ryen W. White, ..., and Qi Guo ACM Transactions on Information Systems (TOIS) 2017 - 8 citations - Show abstract - Cite 7.7% topic match

7.6%

0.8

2014

[119] Behavioural Affect and Cognitive Effects of Time-pressure and Justification Requirement in Software Acquisition: Evidence from an Eye-Tracking Experiment Dennis D. Fehrenbacher and Stephen Smith Americas Conference on Information Systems 2014 - 8 citations - Show abstract - Cite 7.6% topic match

7.5%

0.0

2009

[120] Issues of Real Time Information Retrieval in Large, Dynamic and Heterogeneous Search Spaces John Korah Journal Not Provided 2009 - 0 citations - Show abstract - Cite 7.5% topic match

Examines real-time information retrieval challenges. Highlights inefficiency of offline indexing in dynamic search environments. Suggests need for advanced resource allocation strategies; doesn't specifically address query time impacts on user experience.

Examines real-time information retrieval challenges. Highlights inefficiency of offline indexing in dynamic search environments. Suggests need for advanced resource allocation strategies; doesn't specifically address query time impacts on user experience.

7.5%

0.9

1997

[121] System response time and readiness for task execution the optimum duration of inter-task delays O. Kohlisch and W. Kuhmann Ergonomics 1997 - 25 citations - Show abstract - Cite 7.5% topic match

7.5%

0.1

2009

[122] Modeling search response time Dan Zhang and Luo Si Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval 2009 - 1 citations - Show abstract - Cite 7.5% topic match

7.4%

6.4

2013

[123] Efficient and effective retrieval using selective pruning N. Tonellotto, ..., and I. Ounis Proceedings of the sixth ACM international conference on Web search and data mining 2013 - 73 citations - Show abstract - Cite 7.4% topic match

7.4%

0.3

2008

[124] The Effects of Scannability on Information Search : An Online Experiment P. Gray, ..., and E. O. ́. Neil Journal Not Provided 2008 - 5 citations - Show abstract - Cite 7.4% topic match

7.4%

13.0

2004

[125] Web Site Delays: How Tolerant are Users? D. Galletta, ..., and P. Polak J. Assoc. Inf. Syst. 2004 - 268 citations - Show abstract - Cite 7.4% topic match

7.4%

0.4

2005

[126] SPN Model for Web Prefetching and Caching Lei Shi, ..., and Zhimin Gu 2005 First International Conference on Semantics, Knowledge and Grid 2005 - 8 citations - Show abstract - Cite 7.4% topic match

7.3%

8.8

1984

[127] Response time and display rate in human performance with computers B. Shneiderman ACM Comput. Surv. 1984 - 353 citations - Show abstract - Cite 7.3% topic match

7.3%

0.5

1981

[128] Response Times to User Activities in Interactive Man/Machine Computer Systems G. Gallaway Proceedings of the Human Factors and Ergonomics Society Annual Meeting 1981 - 22 citations - Show abstract - Cite 7.3% topic match

7.3%

1.0

2018

[129] A Reflection on the Applicability of Google Scholar as a Tool for Comprehensive Retrieval in Bibliometric Research and Systematic Reviews M. Houshyar and H. Sotudeh International Journal of Information Science and Management 2018 - 6 citations - Show abstract - Cite 7.3% topic match

7.2%

0.4

2008

[130] An investigation on a community's web search variability Mingfang Wu, ..., and J. Zobel Australasian Computer Science Conference 2008 - 6 citations - Show abstract - Cite 7.2% topic match

7.2%

0.0

2021

[131] Effects of Time Constraints and Search Results Presentation on Web Search M. Beijen Journal Not Provided 2021 - 0 citations - Show abstract - Cite Abstract: Everybody experiences time constraints in their daytoday lives. These time constraints may induce stress, possibly influencing our judgment abilities and behavior. In contemporary daily life, search en gines are regularly consulted to look for information online with searchers commonly experiencing time constraints in the web search process. With an increasing percentage of the global population having access to the internet and thus a search engine, more people will experience being timeconstrained during the search process. The generally unfavorable consequences of these time constraints have been examined in vari ous stages of the search process. Despite the importance of this issue, how the design of the web page showing the search results, or the Search Engine Results Page (SERP), may benefit in time constrained web searches constitutes a substantial and important knowledge gap that this work aims to address. Concretely, we investigate how different time constraints influence task performance and search behavior. Consequently, we aim to determine to what extent various SERP interfaces are sus ceptible to the effects of time constraints. We also want to know how different SERP interfaces impact user experience and we examine to what extent affinity for technology interaction moderates the rela tionship between time constraint and task performance. We aim to address these questions through a crowdsourced 4 (SERP interfaces) × 4 (time con straints) betweensubjects factorial design user study in which participants are tasked with searching the web to find a list of arguments supporting or opposing a controversial topic. To examine to what extent user interfaces are susceptible to the effects of time constraints, participants make use of a mock search system displaying one of four SERP interfaces depending on the experimental condition: a list interface (baseline, traditional interface), a grid interface, a listlike interface without the snippet, and an interface with the snippet placed to the right of other data. The used time constraints are 2, 5, and 8 minutes in addition to a condition without a time constraint. The effects of SERP interfaces and time constraints are evaluated in terms of task performance metrics, search behavior, and user experience. Results have shown that task performance is considerably decreased by stricter time constraints. Also, as time constraints tightened, the rate at which participants issued queries increased. Exploratory results suggest this comes as the cost of reduced depth to which individuals click on results in the ranked list. As for the interaction between SERP interfaces and time constraints, SERP interfaces have not been found to be susceptible to the effects of time constraints. Interestingly, user experience was neither worsened nor improved because of the experimental SERP interfaces and affinity for technology interaction was not found to play a moderating role in the relationship between time constraints and task performance. 7.2% topic match

7.1%

0.0

2010

[132] The effect of behavioral and emotional change on usage mode and response-delay situation Hyounggi Joo, ..., and Kwang-hee Han Journal Not Provided 2010 - 0 citations - Show abstract - Cite 7.1% topic match

7.1%

0.8

2005

[133] Checking Under the Hood J. Adlington and Chris Benda Internet Reference Services Quarterly 2005 - 16 citations - Show abstract - Cite 7.1% topic match

7.0%

0.8

2012

[134] Response latency in stated choice experiments: impact on preference, variance and processing heterogeneity D. Campbell, ..., and S. B. Olsen Journal Not Provided 2012 - 10 citations - Show abstract - Cite 7.0% topic match

7.0%

10.5

2009

[135] Google Scholar’s Ranking Algorithm : An Introductory Overview Jöran Beel and Bela Gipp Journal Not Provided 2009 - 163 citations - Show abstract - Cite 7.0% topic match

Provides analysis on Google Scholar's ranking algorithm. Shows citation count is the most significant factor in ranking. Doesn't directly address search query time or user experience impacts.

Provides analysis on Google Scholar's ranking algorithm. Shows citation count is the most significant factor in ranking. Doesn't directly address search query time or user experience impacts.

7.0%

0.1

2000

[136] The Effect of Download Time on Consumer Attitude Toward the Retailer in eCommerce Gregory M. Rose, ..., and John D Lees Journal Not Provided 2000 - 3 citations - Show abstract - Cite 7.0% topic match

7.0%

2.4

2016

[137] Rain or shine? Forecasting search process performance in exploratory search tasks C. Shah, ..., and Roberto I. González-Ibáñez Journal of the Association for Information Science and Technology 2016 - 19 citations - Show abstract - Cite 7.0% topic match

6.9%

0.0

2015

[138] Narrowing down the debugging space of slow search response time Dapeng Liu, ..., and Mei Feng 2015 IEEE 34th International Performance Computing and Communications Conference (IPCCC) 2015 - 0 citations - Show abstract - Cite 6.9% topic match

6.9%

1.5

2014

[139] What does time constraint mean to information searchers? Chang Liu, ..., and Lu Zhang Proceedings of the 5th Information Interaction in Context Symposium 2014 - 15 citations - Show abstract - Cite 6.9% topic match

6.9%

0.3

2013

[140] Efficient query processing in distributed search engines Simon Jonassen SIGIR Forum 2013 - 3 citations - Show abstract - Cite Abstract: Web search engines have to deal with a rapidly increasing amount of information, high query loads and tight performance constraints. The success of a search engine depends on the speed with which it answers queries (efficiency) and the quality of its answers (effectiveness). These two metrics have a large impact on the operational costs of the search engine and the overall user satisfaction, which determine the revenue of the search engine. In this context, any improvement in query processing efficiency can reduce the operational costs and improve user satisfaction, hence improve the overall benefit. In this thesis, we elaborate on query processing efficiency, address several problems within partitioned query processing, pruning and caching and propose several novel techniques: First, we look at term-wise partitioned indexes and address the main limitations of the state-of-the-art query processing methods. Our first approach combines the advantage of pipelined and traditional (non-pipelined) query processing. This approach assumes one disk access per posting list and traditional term-at-a-time processing. For the second approach, we follow an alternative direction and look at document-at-a-time processing of sub-queries and skipping. Subsequently, we present several skipping extensions to pipelined query processing, which as we show can improve the query processing performance and/or the quality of results. Then, we extend one of these methods with intra-query parallelism, which as we show can improve the performance at low query loads. Second, we look at skipping and pruning optimizations designed for a monolithic index. We present an efficient self-skipping inverted index designed for modern index compression methods and several query processing optimizations. We show that these optimizations can provide a significant speed-up compared to a full (non-pruned) evaluation and reduce the performance gap between disjunctive (OR) and conjunctive (AND) queries. We also propose a linear programming optimization that can further improve the I/O, decompression and computation efficiency of Max-Score. Third, we elaborate on caching in Web search engines in two independent contributions. First, we present an analytical model that finds the optimal split in a static memory-based two-level cache. Second, we present several strategies for selecting, ordering and scheduling prefetch queries and demonstrate that these can improve the efficiency and effectiveness of Web search engines. We carefully evaluate our ideas either using a real implementation or by simulation using real-world text collections and query logs. Most of the proposed techniques are found to improve the state-of-the-art in the conducted empirical studies. However, the implications and applicability of these techniques in practice need further evaluation in real-life settings. This dissertation was completed at the Department of Computer and Information Science at the Norwegian University of Science and Technology (NTNU) under advise of Prof. Svein Erik Bratsberg, Dr. Øystein Torbjørnsen and Dr. Magnus Lie Hetland. Some of the work was done in collaboration with Yahoo! Research Barcelona and mentored by Prof. Ricardo Baeza-Yates and Dr. B. Barla Cambazoglu. Prof. Alistair Moffat (University of Melbourne), Dr. Christina Lioma (University of Copenhagen) and Prof. Kjell Bratsbergsengen (NTNU) served as dissertation committee member. Available online at: http://www.idi.ntnu.no/research/doctor_theses/simonj.pdf. 6.9% topic match

6.9%

4.1

2005

[141] On‐line waiting: The role of download time and other important predictors on attitude toward e‐retailers G. Rose, ..., and J. Curran Psychology & Marketing 2005 - 80 citations - Show abstract - Cite 6.9% topic match

6.8%

1.0

2007

[142] GOOGLE SCHOLAR WEBSITE T. Menon ANZ Journal of Surgery 2007 - 17 citations - Show abstract - Cite Abstract: Researchers, academics, students and librarians are facing an increasingly ardent task of searching through multiple sources to find relevant information. Google Scholar provides a search engine for peer-reviewed papers, theses, abstracts, articles from various publishers, universities and other scholarly organizations. Similar to Google web search, Google Scholar provides the advantage of searching all available scholarly work from one single interface. The search results are ranked on several criteria including full text of the article, author, publication and the number of citations for each article. Google scholar offers a wide coverage of topics, but is particularly strong in science and medicine. The company has entered into agreement with open source publishers and hosting services to provide access to text content to Google crawlers. Like its search counterpart, Google Scholar provides a simple and easyto-use search interface. The advanced search provides various options to narrow down the search by author, publication date and subject areas – as well as usage of boolean operators. The search results in Scholar usually provide a link to the abstract of the article and a pay-per-view option for accessing the full text from commercial publications. Google Scholar does have some weaknesses. Vine has pointed out that Google Scholar was a year behind PubMed and that Scholar search is based on partial PubMed dataset provided to Google by National Library of Medicine.1 Jasco’s extensive critique and review of Google Scholar in 2004 showed that far fewer documents were found when compared with the native search interface of the publishers.2 A second review by Jasco in 2005 discussed inadequacies in Scholar’s advanced search options and noted a variation in citedness scores when compared with other search engines.3 In another independent review conducted by Notess in 2005, Google Scholar returned less results compared with PubMed and Scirus (an online free web search launched by Elsevier).4 The reviewers have also noted a lack of transparency from Google about the publishers and content providers that are included in Google Scholar. Based on present published work and my personal experience the three most important criteria for any scholarly work search are currency of content, search options and search result presentation. Currency of content is important to ensure that the results contain most up-to-date information. Search options provide a way to filter the results especially when the result set is large. The third criterion about search result presentation includes several factors, such as search result ranking, ability to sort the results and export/ save functionality. To test the currency of content, I used the keyword search for ‘Varicella Zoster’ in PubMed, which resulted in 5960 articles and 885 reviews. The latest article in the search results was titled ‘Seroprevalence of varicella zoster virus infections in Colombo District, Sri Lanka’ and was published in Indian Journal of Medicine in March 2007. The advanced search using the same keyword in Google Scholar could not find the same article. Similar results were observed using other keywords. The primary search option in Google Scholar is a single text entry window, similar to its web search. The search help section provides some information on the search syntax such as using (author: ‘name’) that can help narrow down the search from the primary search. The advanced search options are also limited only to search keyword, publication, author, date and subject area. The publication keyword syntax like ‘J Biol Chem’ used by Google Scholar can also be a source of confusion and can result in incorrect results. My search for articles from ‘ANZ J Surg’ between 2000 and 2003 showed only 198 results compared with search for articles from ‘ANZ Journal of Surgery’ for the same period that returned 2850 results. The final criterion for evaluation is the search result presentation. The search results presented by Google Scholar are grouped and ranked based on various factors, including the citedness score using a Google search algorithm. Jasco’s comparison of Scholar with other search engines indicates that the Google Scholar’s citation rates may be the most inaccurate.3 Furthermore, there are no options in Google Scholar to export the search results into different formats or save them for later use. Google Scholar provides a very fast and easy search interface for scholarly publication search. However, it does have deficiencies in currency of content, search options and search results presentation. So in conclusion, Google Scholar is useful as a fast and inexpensive search interface for clinicians and libraries that do not have subscriptions to Web of Science or Scopus. 6.8% topic match

6.8%

34.4

2013

[143] Measuring personalization of web search Anikó Hannák, ..., and Christo Wilson Proceedings of the 22nd international conference on World Wide Web 2013 - 386 citations - Show abstract - Cite - PDF 6.8% topic match

6.7%

0.5

2016

[144] Searching for What I Want: Understanding the Impact of Anticipatory Search control on Search Efficiency Fei Liu, ..., and Chee‐Wee Tan European Conference on Information Systems 2016 - 4 citations - Show abstract - Cite 6.7% topic match

6.7%

0.9

2016

[145] User Behavior in Asynchronous Slow Search Ryan Burton and Kevyn Collins-Thompson Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval 2016 - 7 citations - Show abstract - Cite 6.7% topic match

6.6%

0.0

2015

[146] Parallelization and Scheduling Techniques for Managing Tail Latency in Interactive Services Md. E. Haque, ..., and David Meisner Journal Not Provided 2015 - 0 citations - Show abstract - Cite Abstract: Interactive services, such as Web search, recommendations, games, and finance, must respond quickly to satisfy customers. Achieving this goal requires optimizing tail (e.g., 99th+ percentile) latency. Interactive services often show wide variability in service demand; typically, most requests are short but a few long requests may run for much longer. Services can manage tail latency by giving extra resources to long requests and controlling resource usage of short requests. This is challenging because (1) service demand is unknown when requests arrive; (2) blindly giving extra resources to all requests quickly oversubscribes resources; and (3) giving extra resources to the numerous short requests will not improve tail latency. In this thesis, we propose judicious usage of parallelization and scheduling to manage tail latency in interactive services. First, we introduce Few-toMany (FM) incremental parallelization, which dynamically increases parallelism to utilize idle cores for reducing tail latency. FM uses request service demand profiles and hardware parallelism in an offline phase to compute a policy, represented as an interval table, which specifies when and how much software parallelism to add. At runtime, FM adds parallelism as specified by the interval table indexed by dynamic system load and request execution time progress. The longer a request executes, the more parallelism FM adds. Second, we propose exploiting emerging heterogeneous processors with fast cores and energy-efficient slow cores on the same chip. We introduce Adaptive Slow-to-Fast (A2SF) optimization algorithm, which gives powerful cores to long requests by migrating requests from slow to fast cores. We configure threshold-based migration policies using control theory to optimize a range of performance and energy objectives. We evaluate FM and A2SF in Lucene, an open-source enterprise search engine, and Bing, a commercial Web search engine. Our results show that FM and A2SF can significantly improve tail latency. In addition, our results show that A2SF can significantly reduce energy consumption for specific target tail latency. These results show that FM and A2SF are powerful strategies for managing tail latency in interactive services. Defense Committee: Thu D. Nguyen (Chair), Ricardo Bianchini, Abhishek Bhattacharjee, David Meisner (Facebook, External member) 6.6% topic match

6.6%

0.8

2017

[147] Time Limits, Information Search and the Use of Search Assistance Anita Crescenzi, ..., and Jaime Arguello Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval 2017 - 6 citations - Show abstract - Cite 6.6% topic match

6.5%

0.1

2010

[148] Handling Overload Conditions In High Performance Trustworthy Information Retrieval Systems Sumalatha Ramachandran, ..., and V. Ramaraj ArXiv 2010 - 1 citations - Show abstract - Cite - PDF 6.5% topic match

6.5%

0.0

2010

[149] Admission polices for outperforming the efficiency of search engines Hui Li, ..., and Xia Wang 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems 2010 - 0 citations - Show abstract - Cite 6.5% topic match

6.4%

0.0

2010

[150] The influence of cognitive load, physical load and system response time on user productivity and satisfaction with computer systems E. Munson and James R. Dabrowski Journal Not Provided 2010 - 0 citations - Show abstract - Cite Abstract: Forty years of research into computer system response time have produced many results but overly-general conclusions. The general consensus is that for various tasks with a computer, faster response times are generally better than slow ones, but the computer can respond too fast. When computer response is too slow, users tend to become annoyed and frustrated with the computer system and may perform their tasks more slowly. When computer response is too fast, users may adapt to this by working faster at the expense of committing more errors and generally feeling more stress. However, only the most general guidelines exist for which tasks require rapid response and those for which the user will tolerate some delay. The present study was undertaken to further determine how specific combinations of mental and physical load contribute to an optimal system response time. A generic data-entry task was systematically manipulated to vary the amount of mental and physical effort a subject experienced when engaged in that task under a variety of delay levels in the hopes of identifying how changes in task difficulty interacted with delay to cause users to perform optimally and be the most satisfied with the system. While changes in task difficulty had a significant impact on several measures of user performance and satisfaction, no interaction was found between various levels of task difficulty and levels of delay with respect to how users performed on the task or how they felt about the system. This calls into question the conventional wisdom in the field that for more difficult tasks, users will tolerate or even prefer longer delays whereas for simpler tasks users will be more sensitive to delays and those delays will have a more significant impact on their performance. In light of these results, a new cost-based model of human-computer interaction is proposed in which delay is viewed as an integral part of the human-computer interaction rather than being viewed as somehow separate from the entire process. 6.4% topic match

6.4%

10.4

1998

[151] How Tolerable is Delay? Consumers’ Evaluations of Internet Web Sites after Waiting B. Dellaert and B. Kahn Journal Not Provided 1998 - 276 citations - Show abstract - Cite 6.4% topic match

6.4%

1.8

2012

[152] What does time spent on searching indicate? Pia Borlund, ..., and Katriina Byström International Conference on Information Interaction in Context 2012 - 22 citations - Show abstract - Cite 6.4% topic match

6.4%

0.1

2013

[153] Impact of HTTP Object Load Time on Web Browsing QoE P. Paul and Golam Furkani Quraishy Journal Not Provided 2013 - 1 citations - Show abstract - Cite 6.4% topic match

6.3%

2.6

2020

[154] The Role of Latency and Task Complexity in Predicting Visual Search Behavior L. Battle, ..., and M. Stonebraker IEEE Transactions on Visualization and Computer Graphics 2020 - 12 citations - Show abstract - Cite 6.3% topic match

6.3%

0.0

2012

[155] ’ s Ranking Algorithm : An Introductory Overview Joeran Beel Otto-von-Guericke and Bela Gipp Journal Not Provided 2012 - 0 citations - Show abstract - Cite 6.3% topic match

Explores Google Scholar's ranking algorithm. Shows citation counts heavily influence article visibility in search results. Does not directly address search query time or its effects on user experience.

Explores Google Scholar's ranking algorithm. Shows citation counts heavily influence article visibility in search results. Does not directly address search query time or its effects on user experience.

6.3%

7.5

2005

[156] "I still like Google": University student perceptions of searching OPACs and the web Karl Fast and D. G. Campbell ASIS&T Annual Meeting 2005 - 142 citations - Show abstract - Cite 6.3% topic match

6.3%

0.4

2017

[157] TailCut: Power Reduction under Quality and Latency Constraints in Distributed Search Systems C. Chou, ..., and Shaolei Ren 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) 2017 - 3 citations - Show abstract - Cite 6.3% topic match

6.3%

3.0

2008

[158] User Preference and Search Engine Latency J. Brutlag, ..., and M. Stone Journal Not Provided 2008 - 49 citations - Show abstract - Cite 6.3% topic match

6.2%

0.0

2015

[159] On-Lib: An Application and Analysis ofFuzzy-Fast Query Searching and Clusteringon Library Database Ashritha K.P and Sudheer Shetty International Journal of Innovative Research in Computer and Communication Engineering 2015 - 0 citations - Show abstract - Cite 6.2% topic match

6.2%

4.1

2013

[160] Time pressure, user satisfaction and task difficulty Anita Crescenzi, ..., and Jaime Arguello ASIS&T Annual Meeting 2013 - 44 citations - Show abstract - Cite 6.2% topic match

6.2%

0.0

1988

[161] Does system response time disrupt working memory E. Schultz, ..., and A. M. Alvare Journal Not Provided 1988 - 0 citations - Show abstract - Cite 6.2% topic match

6.2%

0.8

1998

[162] The world wide wait: where does the time go? C. Allison, ..., and Jose Serrano Proceedings. 24th EUROMICRO Conference (Cat. No.98EX204) 1998 - 20 citations - Show abstract - Cite 6.2% topic match

6.1%

0.4

2011

[163] The Effect of Feedback on Web Site Delay: A Perceptual and Physiological Study Hong Sheng and Nicholas S. Lockwood Journal Not Provided 2011 - 6 citations - Show abstract - Cite 6.1% topic match

6.1%

3.8

2016

[164] TPC: Target-Driven Parallelism Combining Prediction and Correction to Reduce Tail Latency in Interactive Services Myeongjae Jeon, ..., and A. Cox Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems 2016 - 32 citations - Show abstract - Cite 6.1% topic match

6.1%

0.5

2016

[165] Using the Crowd to Improve Search Result Ranking and the Search Experience Yubin Kim, ..., and J. Teevan ACM Transactions on Intelligent Systems and Technology (TIST) 2016 - 4 citations - Show abstract - Cite 6.1% topic match

6.1%

1.2

2003

[166] A belief-updating process for minimizing waiting time in multiple waiting-time events: Application in website design B. Weinberg, ..., and R. Hanna Journal of Interactive Marketing 2003 - 26 citations - Show abstract - Cite 6.1% topic match

6.0%

0.0

2006

[167] A Human Factors Experiment With Students’ Google Searching S. M. Zabed Ahmed and T. Hossain Journal Not Provided 2006 - 0 citations - Show abstract - Cite 6.0% topic match

6.0%

1.8

2020

[168] How searching under time pressure impacts clinical decision making A. van der Vegt, ..., and Anthony J Deacon Journal of the Medical Library Association : JMLA 2020 - 7 citations - Show abstract - Cite 6.0% topic match

6.0%

0.0

2014

[169] The Impact of Waiting Time Distributions on QoE of Task-Based Web Browsing Sessions Nazrul Islam and Vijaya John David Elepe Journal Not Provided 2014 - 0 citations - Show abstract - Cite 6.0% topic match

6.0%

2.4

2022

[170] Are We There Yet? A Decision Framework for Replacing Term Based Retrieval with Dense Retrieval Systems Sebastian Hofstatter, ..., and A. Hanbury ArXiv 2022 - 5 citations - Show abstract - Cite - PDF 6.0% topic match

5.9%

0.1

2010

[171] Perceived and Actual Web Page Loading Delay: What Difference Do Predictive Feedback Information (PFI) Cues Make? Mohamed Louadi and I. B. Ali J. Inf. Technol. Res. 2010 - 2 citations - Show abstract - Cite 5.9% topic match

5.8%

0.0

2008

[172] A methodology for evaluation of web-based scholarship David P. Dailey Conference on Information Technology Education 2008 - 0 citations - Show abstract - Cite 5.8% topic match

5.8%

1.9

1989

[173] Experimental investigation of stress-inducing properties of system response times. Werner Kuhmann Ergonomics 1989 - 68 citations - Show abstract - Cite 5.8% topic match

5.8%

0.0

2014

[174] Query revision during cluster based search on large unstructured corpora V. Deolalikar 2014 IEEE International Conference on Big Data (Big Data) 2014 - 0 citations - Show abstract - Cite Abstract: We investigate a frequently occurring issue in search (retrieval) in the age of big unstructured data. Searches conducted on large unstructured corpora result in long results lists. Such results lists are often clustered and reranked for ease of navigation. Should a query be revised during time-critical examinations of such long cluster based reranked lists? This question arises naturally during early stages of commercially important applications of IR such as eDiscovery, but has not yet been given any research attention. Four factors compound the difficulty of this question in the setting of eDiscovery: (a) the query sources (the technical experts) are different from the legal staff that are actually executing the query and using the retrieval system, (b) the retrieved lists for each query tend to be very long, and (c) the user might be accessing these retrieved results through a clustering interface, and (c) all decisions must be transparent and easy to explain due to the litigious nature of the application. Analogous difficulties arise in other applications involving search over large unstructured corpora. We introduce a framework to help users make the decision of “whether to revise.” Our framework consists of two components. First, we introduce a “limited view” which is a summary of a long cluster-based reranked list. This is the first input to the user. This provides the user a summary of the long cluster-based list. Second, we construct query predictors for this limited view, and provide their prediction as a second input to the user. This prediction is used to corroborate the inspection of the summary limited view. The proposed combination of a limited view and query performance prediction can assist search staff in determining whether to pursue an expensive query revision or not, as well as save precious time by precluding inspections of lists with very few relevant documents during the early stages of commercially important applications such as eDiscovery. 5.8% topic match

5.8%

0.0

2010

[175] Techniques for improving efficiency and scalability for the integration of information retrieval and databases Hengzhi Wu Journal Not Provided 2010 - 0 citations - Show abstract - Cite Abstract: This thesis is on the topic of integration of Information Retrieval (IR) and Databases (DB), with particular focuses on improving efficiency and scalability of integrated IR and DB technology (IR+DB). The main purpose of this study is to develop efficient and scalable techniques for supporting integrated IR and DB technology, which is a popular approach today for handling complex queries over text and structured data. Our specific interest in this thesis is how to efficiently handle queries over large-scale text and structured data. The work is based on a technology that integrates probability theory and relational algebra, where retrievals for text and data are to be expressed in probabilistic logical programs such as probabilistic relational algebra or probabilistic Datalog. To support efficient processing of probabilistic logical programs, we proposed three optimization techniques that focus on aspects covered logical and physical layers, which include: scoring-driven query optimization using scoring expression, query processing with top-k incorporated pipeline, and indexing with relational inverted index. Specifically, scoring expressions are proposed for expressing the scoring or probabilistic semantics of implied scoring functions of PRA expressions, so that efficient query execution plan can be generated by rule-based scoring-driven optimizer. Secondly, to balance efficiency and effectiveness so that to improve query response time, we studied methods for incorporating topk algorithms into pipelined query execution engine for IR+DB systems. Thirdly, the proposed relational inverted index integrates IR-style inverted index and DB-style tuple-based index, which can be used to support efficient probability estimation and aggregation as well as conventional relational operations. Experiments were carried out to investigate the performances of proposed techniques. Experimental results showed that the efficiency and scalability of an IR+DB prototype have been improved, while the system can handle queries efficiently on considerable large data sets for a number of IR tasks. 5.8% topic match

5.8%

0.0

2010

[176] Söktjänster för akademiskt bruk. En jämförande undersökning mellan Google, Google Scholar och Scirus. Karl Bjernestad Journal Not Provided 2010 - 0 citations - Show abstract - Cite 5.8% topic match

5.8%

0.3

2017

[177] Luo, Cheng and Liu, Yiqun and Sakai, Tetsuya and Zhou, Ke and Zhang, Fan and Li, Xue and Ma, Shaoping (2017) Does document relevance affect the searcher's perception 0f time? In: 10th ACM International Conference on Web Search and Data No author found Journal Not Provided 2017 - 2 citations - Show abstract - Cite 5.8% topic match

5.7%

0.5

2015

[178] Characterization and analysis of a web search benchmark Zacharias Hadjilambrou, ..., and Yiannakis Sazeides 2015 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS) 2015 - 5 citations - Show abstract - Cite 5.7% topic match

5.7%

0.4

1984

[179] System response time and user performance G. Dannenbring IEEE Transactions on Systems, Man, and Cybernetics 1984 - 15 citations - Show abstract - Cite 5.7% topic match

5.7%

7.2

2018

[180] Efficient Query Processing for Scalable Web Search N. Tonellotto, ..., and I. Ounis Found. Trends Inf. Retr. 2018 - 40 citations - Show abstract - Cite Abstract: Search engines are exceptionally important tools for accessing information in today’s world. In satisfying the information needs of millions of users, the effectiveness (the quality of the search results) and the efficiency (the speed at which the results are returned to the users) of a search engine are two goals that form a natural trade-off, as techniques that improve the effectiveness of the search engine can also make it less efficient. Meanwhile, search engines continue to rapidly evolve, with larger indexes, more complex retrieval strategies and growing query volumes. Hence, there is a need for the development of efficient query processing infrastructures that make appropriate sacrifices in effectiveness in order to make gains in efficiency. This survey comprehensively reviews the foundations of search engines, from index layouts to basic term-at-a-time (TAAT) and document-at-a-time (DAAT) query processing strategies, while also providing the latest trends in the literature in efficient query processing, including the coherent and systematic reviews of techniques such as dynamic pruning and impact-sorted posting lists as well as their variants and optimisations. Our explanations of query processing strategies, for instance the WAND and BMW dynamic pruning algorithms, are presented with illustrative figures showing how the processing state changes as the algorithms progress. Moreover, acknowledging the recent trends in applying a cascading infrastructure within search systems, this survey describes techniques for efficiently integrating effective learned models, such as those obtained from learning-torank techniques. The survey also covers the selective application of query processing techniques, often achieved by predicting the response times of the search engine (known as query efficiency prediction), and making per-query tradeoffs between efficiency and effectiveness to ensure that the required retrieval speed targets can be met. Finally, the survey concludes with a summary of open directions in efficient search infrastructures, namely the use of signatures, real-time, energy-efficient and modern hardware & software architectures. 5.7% topic match

5.7%

0.4

1981

[181] System Response Time and Problem Solving Behavior H. Bergman, ..., and H. S. Koelega Proceedings of the Human Factors and Ergonomics Society Annual Meeting 1981 - 18 citations - Show abstract - Cite 5.7% topic match

5.6%

1.0

2013

[182] Cross-Examining Google Scholar K. Antell, ..., and K. O'Kelly Reference and User Services Quarterly 2013 - 11 citations - Show abstract - Cite 5.6% topic match

5.6%

11.9

2012

[183] “Time is bandwidth”? Narrowing the gap between subjective time perception and Quality of Experience Sebastian Egger, ..., and R. Schatz 2012 IEEE International Conference on Communications (ICC) 2012 - 144 citations - Show abstract - Cite 5.6% topic match

5.6%

0.9

2022

[184] Semantic Modelling of Document Focus-Time for Temporal Information Retrieval Lirong Zhang, ..., and Haitao Yu Companion Proceedings of the Web Conference 2022 2022 - 2 citations - Show abstract - Cite 5.6% topic match

5.5%

0.1

2015

[185] Query Scheduling Techniques and Power-Latency Trade-off Model for Large-Scale Search Engines Ana Freire ACM SIGIR Forum 2015 - 1 citations - Show abstract - Cite 5.5% topic match

5.5%

0.0

2009

[186] Measuring and Injecting Latency in Web Apps Adam Goode, ..., and M. Satyanarayanan Journal Not Provided 2009 - 0 citations - Show abstract - Cite 5.5% topic match

5.5%

0.8

2014

[187] Document Prioritization for Scalable Query Processing Hao Wu and Hui Fang Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management 2014 - 8 citations - Show abstract - Cite 5.5% topic match

5.5%

0.3

2020

[188] Examination of Effects of Time Constraint and Task Type on Users’ Query Typing Behaviors Hanqi Peng, ..., and Chang Liu Data and Information Management 2020 - 1 citations - Show abstract - Cite 5.5% topic match

5.5%

14.4

2012

[189] Waiting times in quality of experience for web based services Sebastian Egger, ..., and M. Fiedler 2012 Fourth International Workshop on Quality of Multimedia Experience 2012 - 174 citations - Show abstract - Cite 5.5% topic match

5.4%

5.5

2001

[190] The Effect of Download Time on Consumer Attitude Toward the e-Service Retailer G. Rose and D. Straub e-Service Journal 2001 - 125 citations - Show abstract - Cite 5.4% topic match

5.4%

2.8

2016

[191] Googling the news1 Jacob Ørmen Digital Journalism 2016 - 24 citations - Show abstract - Cite 5.4% topic match

5.4%

0.2

2007

[192] Internet Search by Active Feedback Ray-I Chang and Yu-Ying Wu 2007 International Symposium on Communications and Information Technologies 2007 - 3 citations - Show abstract - Cite 5.4% topic match

5.4%

0.0

2014

[193] Proactive search: Using outcome-based dynamic nearest-neighbor recommendation algorithms to improve search engine efficacy Christopher Shaun Wagner Journal Not Provided 2014 - 0 citations - Show abstract - Cite 5.4% topic match

5.3%

0.0

2020

[194] Can Certain Actions in Virtual Space Make a Human Being Impatient? No author found Journal of Psychology and Neuroscience 2020 - 0 citations - Show abstract - Cite 5.3% topic match

5.3%

0.7

2014

[195] In small chunks or all at once? User preferences of network delays in web browsing sessions Nazrul Islam, ..., and M. Fiedler 2014 IEEE International Conference on Communications Workshops (ICC) 2014 - 7 citations - Show abstract - Cite 5.3% topic match

5.3%

0.8

2008

[196] Perceptions of download delays: relation to actual waits, web site abandoning, and stage of delay P. A. Dabholkar and Xiaojing Sheng The Service Industries Journal 2008 - 13 citations - Show abstract - Cite 5.3% topic match

5.3%

0.5

2011

[197] Indexing strategies for graceful degradation of search quality Shuai Ding, ..., and A. Ntoulas Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval 2011 - 7 citations - Show abstract - Cite 5.3% topic match

5.2%

0.2

1969

[198] Initial Experiments on the Effects of System Delay on On-Line Problem-Solving. M. Morfield, ..., and D. Yntema https://doi.org/10.21236/ad0691815 1969 - 12 citations - Show abstract - Cite 5.2% topic match

5.2%

0.8

2017

[199] An analysis of perceived usefulness of Google Scholar by the postgraduate students of the University of Ilorin, Nigeria A. Tella, ..., and A. Tella SA Journal of Information Management 2017 - 6 citations - Show abstract - Cite 5.2% topic match

5.2%

0.2

2018

[200] Efficient Query Processing Infrastructures: A half-day tutorial at SIGIR 2018 N. Tonellotto and Craig Macdonald The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval 2018 - 1 citations - Show abstract - Cite 5.2% topic match

5.1%

1.4

2014

[201] On correlation of absence time and search effectiveness Sunandan Chakraborty, ..., and Paul Baecke Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval 2014 - 14 citations - Show abstract - Cite 5.1% topic match

5.1%

2.7

2013

[202] A survey of temporal web search experience Hideo Joho, ..., and Roi Blanco Proceedings of the 22nd International Conference on World Wide Web 2013 - 30 citations - Show abstract - Cite 5.1% topic match

5.0%

0.0

2000

[203] Reading Course Paper Techniques to Improve Upon a User ’ s WWW Experience C. Choi Journal Not Provided 2000 - 0 citations - Show abstract - Cite Abstract: It is projected by the year 2002 there will be over 320 mil lion users on the World Wide Web (WWW), compared to the 100 million users recorded in 1998 [20]. This in crease will place additional pressure on the content providers of the WWW, and in particular to th se web sites that rely on the WWW for revenue. The e-commerce craze has greatly intensified, but how are the e-commerce retailers (e-tailers), such as Amazon.com, Chapters.ca, and other dot-coms going to reta in their customers. The greatest problem for etailers is the reference made by many users to the WW W as being the “World Wide Wait”. With the WWW tripling in a few years time, the delays and conges tion on the WWW will only intensify. The users of the WWW are not concerned with the back end network inf astructures and web servers delivering content, but are only concerned with the “key to glass ” re ponse time. The “key to glass” response time is the amount of time that passes after a user clicks the m ouse button or presses the return key to submit a request for a particular WWW resource, to the time the r equested resource is displayed on the monitor glass. The key time interval for this action to occur i s 10 seconds, as this is the attention span for users focused on a particular piece of dialogue. This value has b een true for the last 30 years from work by [14] to [8]. For a user to be fully satisfied with their WWW experience, measures must be in place to help achieve the 10 seconds constraint. In this paper, we shall investigate a few alternatives that attempt to provide an acceptable Quality of Service (QoS) to the user . We will begin by examining techniques that entail replicated servers in section 2, followed by cachin g techniques in section 3, and lastly improvements on the actual web content that is delivered by the web servers in section 4. Both sections 2 and 3 will be relatively brief compared to section 4, as our researc h is mostly interested in adaptive web content. Sectio n 5 will provide a conclusion to the various research appro aches and how our research coincides with the other research. 5.0% topic match

5.0%

2.2

2011

[204] Refining Recency Search Results with User Click Feedback Taesup Moon, ..., and Yi Chang ArXiv 2011 - 29 citations - Show abstract - Cite - PDF 5.0% topic match

5.0%

21.8

2013

[205] The Google scholar experiment: How to index false papers and manipulate bibliometric indicators Emilio Delgado López-Cózar, ..., and D. Torres-Salinas Journal of the Association for Information Science and Technology 2013 - 237 citations - Show abstract - Cite - PDF 5.0% topic match

5.0%

0.8

2022

[206] The Dark Side of Relevance: The Effect of Non-Relevant Results on Search Behavior Mustafa Abualsaud and Mark D. Smucker Proceedings of the 2022 Conference on Human Information Interaction and Retrieval 2022 - 2 citations - Show abstract - Cite 5.0% topic match

4.9%

5.8

2015

[207] Delayed-Dynamic-Selective (DDS) Prediction for Reducing Extreme Tail Latency in Web Search Saehoon Kim, ..., and Seungjin Choi Proceedings of the Eighth ACM International Conference on Web Search and Data Mining 2015 - 55 citations - Show abstract - Cite 4.9% topic match

4.9%

0.5

2002

[208] Towards time design: pacing of hypertext navigation by system response times Herbert A. Meyer and Michael Hildebrandt CHI '02 Extended Abstracts on Human Factors in Computing Systems 2002 - 12 citations - Show abstract - Cite 4.9% topic match

4.9%

13.4

2007

[209] Comparing test searches in PubMed and Google Scholar. Mary Shultz Journal of the Medical Library Association : JMLA 2007 - 226 citations - Show abstract - Cite Abstract: Google Scholar has been met with both enthusiasm and criticism since its introduction in 2004. This search engine provides a simple way to access “peer-reviewed papers, theses, books, abstracts, and articles from academic publishers' sites, professional societies, preprint repositories, universities and other scholarly organizations” [1]. An obvious strength of Google Scholar is its intuitive interface, as the main search engine interface consists of a simple query box. In contrast, databases, such as PubMed, utilize search interfaces that offer a greater variety of advanced features. These additional features, while powerful, often lead to a complexity that may require a substantial investment of time to master. It has been observed that Google Scholar may allow searchers to “find some resources they can use rather than be frustrated by a database's search screen” [2]. Some even feel that “Google Scholar's simplicity may eventually consume PubMed” [3]. Along with ease of use, Google Scholar carries the familiar “Google” brand name. As Kennedy and Price so aptly stated, “College students AND professors might not know that library databases exist, but they sure know Google” [4]. The familiarity of Google may allow librarians and educators to ease students into the scholarly searching process by starting with Google Scholar and eventually moving to more complex systems. Felter noted that “as researchers work with Google Scholar and reach limitations of searching capabilities and options, they may become more receptive to other products” [5]. Google Scholar is also thought to provide increased access to gray literature [2], as it retrieves more than journal articles and includes preprint archives, conference proceedings, and institutional repositories [6]. Google Scholar also includes links to the online collections of some academic libraries. Including these access points in Google Scholar retrieval sets may ultimately help more users reach more of their own institution's subscriptions [7]. While its advantages are substantial, Google Scholar is not without flaws. The shortcomings of the system and its search interface have been well documented in the literature and include lack of reliable advanced search functions, lack of controlled vocabulary, and issues regarding scope of coverage and currency. Table 1 summarizes some of the reported criticisms of Google Scholar. Table 1 Criticisms of Google Scholar Vine found that while Google Scholar pulls in data from PubMed, many PubMed records are missing [20], and that Google Scholar also lacks features available in MEDLINE [12]. Others have noted that Google Scholar should not be the first or sole choice when searching for patient care information, clinical trials, or literature reviews [23,24]. Thorough review and testing of Google Scholar, being an approach similar to that used to evaluate licensed resources, is necessary to better understand its strengths and limitations. As Jacso states, “professional searchers must do sample test searches and correctly interpret the results to corroborate claims and get factual information about databases” [18]. This paper compares and contrasts a variety of test searches in PubMed and Google Scholar to gain a better understanding of Google Scholar's searching capabilities. 4.9% topic match

4.8%

0.0

2003

[210] DELAY AND PROCRASTINATION IN SEARCH Scott M. Gilpatric Journal Not Provided 2003 - 0 citations - Show abstract - Cite 4.8% topic match

4.8%

0.7

2014

[211] The influence of task type on search experience from the perspective of time constraint Chang Liu, ..., and Yu Zhao ASIS&T Annual Meeting 2014 - 7 citations - Show abstract - Cite 4.8% topic match

4.7%

1.1

2022

[212] Query Transformation for Approximate Query Processing Using Synthetic Data from Deep Generative Models Taewhi Lee, ..., and Sung-Soo Kim 2022 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia) 2022 - 2 citations - Show abstract - Cite 4.7% topic match

4.7%

0.7

2013

[213] A PERSONALIZED WEB SEARCH BASED ON USER PROFILE AND USER CLICKS S. Vanitha and India U.S. Consulate Chennai Journal Not Provided 2013 - 8 citations - Show abstract - Cite 4.7% topic match

4.7%

6.5

2008

[214] Improved query difficulty prediction for the web C. Hauff, ..., and R. Baeza-Yates International Conference on Information and Knowledge Management 2008 - 102 citations - Show abstract - Cite 4.7% topic match

4.7%

6.9

2008

[215] The Retrieval Effectiveness of Web Search Engines: Considering Results Descriptions D. Lewandowski J. Documentation 2008 - 109 citations - Show abstract - Cite - PDF 4.7% topic match

4.7%

0.5

2019

[216] Adaptation in Information Search and Decision-Making under Time Pressure Anita Crescenzi Journal Not Provided 2019 - 3 citations - Show abstract - Cite 4.7% topic match

4.7%

2.0

2015

[217] An Initial Investigation into Fixed and Adaptive Stopping Strategies David Maxwell, ..., and Heikki Keskustalo Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval 2015 - 18 citations - Show abstract - Cite 4.7% topic match

4.6%

3.3

2019

[218] The Effects of Working Memory, Perceptual Speed, and Inhibition in Aggregated Search Jaime Arguello and Bogeum Choi ACM Transactions on Information Systems (TOIS) 2019 - 17 citations - Show abstract - Cite Abstract: Prior work has studied how different characteristics of individual users (e.g., personality traits and cognitive abilities) can impact search behaviors and outcomes. We report on a laboratory study (N = 32) that investigated the effects of three different cognitive abilities (perceptual speed, working memory, and inhibition) in the context of aggregated search. Aggregated search systems combine results from multiple heterogeneous sources (or verticals) in a unified presentation. Participants in our study interacted with two different aggregated search interfaces (a within-subjects design) that differed based on the extent to which the layout distinguished between results originating from different verticals. The interleaved interface merged results from different verticals in a fairly unconstrained fashion. Conversely, the blocked interface displayed results from the same vertical as a group, displayed each group of vertical results in the same region on the SERP for every query, and used a border around each group of vertical results to help distinguish among results from different sources. We investigated three research questions (RQ1--RQ3). Specifically, we investigated the effects of the interface condition and each cognitive ability on three types of outcomes: (RQ1) participants’ levels of workload, (RQ2) participants’ levels of user engagement, and (RQ3) participants’ search behaviors. Our results found different main and interaction effects. Perceptual speed and inhibition did not significantly affect participants’ workload and user engagement but significantly affected their search behaviors. Specifically, with the interleaved interface, participants with lower perceptual speed had more difficulty finding relevant results on the SERP, and participants with lower inhibitory attention control searched at a slower pace. Working memory did not have a strong effect on participants’ behaviors but had several significant effects on the levels of workload and user engagement reported by participants. Specifically, participants with lower working memory reported higher levels of workload and lower levels of user engagement. We discuss implications of our results for designing aggregated search interfaces that are well suited for users with different cognitive abilities. 4.6% topic match

4.6%

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2005

[219] Waiting for service on the internet: Defining the phenomenon and identifying the situations Gerard Ryan and M. Valverde Internet Res. 2005 - 39 citations - Show abstract - Cite 4.6% topic match

4.6%

6.9

2000

[220] Don't keep your Internet customers waiting too long at the (virtual) front door B. Weinberg Journal of Interactive Marketing 2000 - 163 citations - Show abstract - Cite Abstract: One of the primary e-commerce challenges on the World Wide Web is when users experience intolerably long waits for a website's homepage to load. Zona Research, Inc. estimates that over $4 billion in lost revenue is due to slow downloads over the Internet. When the loading time of a homepage exceeds the maximum amount of time that a Web user is willing to wait, a Web user will either redirect the web-browser to an alternative (e.g., competitor's) website or quit using the Web; an opportunity, at the moment and perhaps forever, is lost to not only serve, influence, or interact with, a potential customer, but also to advance the growth of e-commerce. Given the important role of a homepage as a portal to a website or to a host of websites, it is critical that a homepage design consider not only appearance and functionality, but also loading time. The Internet industry has been devoting significant attention to solving the waiting time problem with approaches that are technical or operational in nature, with those most promising being extremely expensive and time consuming to employ (e.g., fiber optic cable). These approaches have not, up to this point in time, yielded the desired results. This research describes a complementary marketing approach to reducing the negative impact of the waiting time problem; one that is based on the psychological theorizing of “anchoring and adjustment,” with implications that would be relatively inexpensive to implement. In experiments where all Web users experienced the same actual wait for a homepage to load, those exposed to a shorter waiting time anchor, both perceived as shorter the waiting time for, and evaluated as higher the quality of, a homepage; and when the waiting time anchor was less than the actual waiting time, the perceived waiting time was less than the actual waiting time. In addition, those exposed to the smaller waiting time anchor were more likely to continue searching the associated website as opposed to searching a different website. 4.6% topic match

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2013

[221] Survey on Web Search Results Personalization Techniques V. Kakulapati, ..., and S. Jena Journal Not Provided 2013 - 0 citations - Show abstract - Cite 4.6% topic match

4.5%

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2009

[222] Efficiency trade-offs in two-tier web search systems R. Baeza-Yates, ..., and C. Hauff Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval 2009 - 45 citations - Show abstract - Cite 4.5% topic match

4.5%

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2008

[223] University of Innsbruck Working Papers in Economics and Statistics Searching for a better deal – On the influence of group decision making , time pressure and gender in a search experiment Marcela Ibanez, ..., and Matthias Sutter Journal Not Provided 2008 - 0 citations - Show abstract - Cite 4.5% topic match

4.4%

1.3

2013

[224] Literature searches with Google Scholar: Knowing what you are and are not getting Jerome V. DeGraff, ..., and H. Charles Romesburg Gsa Today 2013 - 14 citations - Show abstract - Cite Abstract: Whether you are a student developing a senior thesis or a geoscientist preparing a research proposal, finding relevant concepts, data, and information produced by other geoscientists is a crucial step to eventual success. The quote, “If I have seen farther it is by standing on the shoulders of giants,” attributed to Sir Isaac Newton, acknowledges this fact. Our expanding knowledge base, increasing professional specialization, and greater involvement in interdisciplinary studies make it less and less likely we will know all the useful information important to any study we may consider undertaking. For this reason, we turn to scholarly literature to remedy any important deficiencies. For both students and researchers, this has increasingly meant employing Google Scholar. SearchiNG LiteratUre iN the DiGitaL aGe Some of the same technological changes that enhance our ability to collect relevant data also facilitate our ability to search scholarly literature. Abstracting and indexing of published literature is now provided in computerized, searchable bibliographic databases. These include both discipline-specific databases, such as GeoRef, produced by the American Geological Institute, and multidisciplinary ones, such as Web of Science from Thomson Reuters and Scopus from Elsevier (Walters, 2011). Internet-based platforms are available for accessing these databases. In November 2007, Google Scholar was introduced as another means for geoscientists to conduct a computer-based literature search. It is an Internet search engine rather than a computerized bibliographic database. Access is through the widely used Google Internet portal. Like all new methods and ideas, thoroughly examining the strengths and limitations of Google Scholar ensures we understand what it does or does not actually deliver. Before looking closely at Google Scholar’s use in literature searches, it is important to understand how searching differs between Google and Google Scholar. Both are search engines Literature searches with Google Scholar: Knowing what you are and are not getting owned by Google Inc. and both use proprietary software to identify Web-based links relevant to the search terms entered by the user. The terms entered into a Google search initiate a hunt through all publically accessible files on Web servers connected to the Internet that match those words. The Google Scholar search engine utilizes a variant of this software that searches for the user’s terms within only scholarly publications as defined by the source servers; e.g., universities and scientific publishers (Walters, 2011). How the results obtained can differ is demonstrated by a search we conducted on 27 Jan. 2013 using the search term “Indian ocean tsunami.” The Google search returned 6.8 million results with the ten listed on the first screen page including an entry about the 2004 event on Wikipedia, a news item on National Geographic’s website, and reports from six major national and international news organizations. Some news items related to the 2004 event and others to the tsunami watch that occurred after the 11 Apr. 2012 earthquake. The remaining two entries consisted of collected still images and videos about the tsunami. In contrast, Google Scholar returned a comparatively modest 28,000 results. Except for four of the ten entries, the first page provided links to articles in scholarly journals ranging from Nature to the International Journal of Hospitality Management (Elsevier). The other entries were technical pages on a university website and technical reports on websites established by international donors for relief efforts and a government disaster response agency. This illustrates the very different search algorithms employed by Google and Google Scholar in terms of result numbers, content, and sources. It is worth noting that searches using this term over time returned widely differing result numbers for Google but not for Google Scholar. This reflects the more dynamic nature of Internet content as a whole compared to that part defined as scholarly content by Google Scholar. MechaNicS OF bibLiOGraPhic DatabaSeS aND GOOGLe SchOLar To fully explore any advantages or disadvantages of Google Scholar requires understanding how a search engine differs from a computerized bibliographic database. The content of bibliographic databases is developed through indexing done by the organization producing them. Indexed entries are added to these databases by organizations’ employees based on a set of criteria related to specific sources and standards. The GeoRef thesaurus is 4.4% topic match

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[225] Journal of Emerging Trends in Computing and Information Sciences Users' Perception of Using Web Search Engines and Their Impact on the Perceived Intention to Reuse the Systems Khamsum Kinley Journal Not Provided None - 3 citations - Show abstract - Cite 4.4% topic match

4.3%

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2013

[226] The effects of subjective time pressure and individual differences on hypotheses generation and action prioritization in police investigations. Laurence Alison, ..., and A. Humphrey Journal of experimental psychology. Applied 2013 - 34 citations - Show abstract - Cite 4.3% topic match

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2020

[227] Personalized query recommendation system : A genetic algorithm approach Debaditya Barman, ..., and N. Chowdhury Journal of Interdisciplinary Mathematics 2020 - 11 citations - Show abstract - Cite 4.3% topic match

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0.9

2010

[228] Naïve judgements of stimulus–response compatibility E. Hoffmann Ergonomics 2010 - 13 citations - Show abstract - Cite 4.3% topic match

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2002

[229] Criteria for Evaluating Information Retrieval Systems in Highly Dynamic Environments J. Bar-Ilan Journal Not Provided 2002 - 9 citations - Show abstract - Cite 4.3% topic match

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2020

[230] Up-To-Dateness and Time Response of Selected Web Search Engines: Comparative Study R. G. Bobade Journal Not Provided 2020 - 0 citations - Show abstract - Cite 4.3% topic match

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0.7

2015

[231] Domain-specific queries and Web search personalization: some investigations Van Tien Hoang, ..., and R. Nicola International Workshop on Automated Specification and Verification of Web Sites 2015 - 6 citations - Show abstract - Cite - PDF 4.3% topic match

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1.4

2006

[232] Google Scholar: potentially good for users of academic information F. Friend Journal of Electronic Publishing 2006 - 26 citations - Show abstract - Cite 4.3% topic match

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2018

[233] Batch Processing of Top-k Spatial-Textual Queries F. Choudhury, ..., and T. Sellis ACM Transactions on Spatial Algorithms and Systems (TSAS) 2018 - 10 citations - Show abstract - Cite 4.2% topic match

4.2%

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2008

[234] How do users find things with PubMed?: towards automatic utility evaluation with user simulations Jimmy J. Lin and Mark D. Smucker Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2008 - 40 citations - Show abstract - Cite 4.2% topic match

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2017

[235] Understanding the impact of costly search on market equilibria: A comprehensive review Vangelis Logothetis, ..., and D. Varoutas 2017 Internet of Things Business Models, Users, and Networks 2017 - 0 citations - Show abstract - Cite 4.1% topic match

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2006

[236] Waiting in line for online services: a qualitative study of the user's perspective Gerard Ryan and M. Valverde Information Systems Journal 2006 - 50 citations - Show abstract - Cite 4.1% topic match

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2008

[237] Effects of Google Scholar on examination for novelty of science of research papers Hu Ling Journal Not Provided 2008 - 0 citations - Show abstract - Cite 4.1% topic match

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2007

[238] Briefly Analyzing the Academic Search Engine——Google Scholar Zhou Jia-li Journal of Jiangsu Teachers University of Technology 2007 - 0 citations - Show abstract - Cite 4.1% topic match

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2017

[239] Tail-Tolerant Distributed Search Naama Kraus, ..., and I. Keidar ArXiv 2017 - 0 citations - Show abstract - Cite - PDF 4.0% topic match

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10.6

2017

[240] Designing Progressive and Interactive Analytics Processes for High-Dimensional Data Analysis C. Turkay, ..., and H. Hauser IEEE Transactions on Visualization and Computer Graphics 2017 - 80 citations - Show abstract - Cite 4.0% topic match

4.0%

0.2

2008

[241] Searching for a better deal – On the influence of team decision making , time pressure and gender * M. Ibáñez, ..., and Matthias Sutter Journal Not Provided 2008 - 4 citations - Show abstract - Cite 4.0% topic match

3.9%

0.1

2012

[242] Systematic optimization of search engines for difficult queries Maryam Karimzadehgan Journal Not Provided 2012 - 1 citations - Show abstract - Cite Abstract: With the advent of Web, text information is being generated across the globe at an unfathomable rate and covering countless topics. This dramatic growth in text information and the increasing number of ways people can utilize it has influenced our daily lives in fundamental and profound ways. The widespread and multi-purposed use of search engines is one example of how transformed our lives have become in relation to text information. Although the vast majority of people's information needs can be served very successfully by current search engines, there is still a considerable number of queries that even the best search engines perform poorly on. In this dissertation, we propose a method of optimizing a search engine to better handle such “difficult queries”, which addresses issues and opportunities at three different stages of an interactive search. Specifically, we propose to improve search quality for difficult queries by: (1) Bridging the vocabulary gap by defining a semantic smoothing language model. A query can be difficult because it does not contain the optimal choice of related terms, or lacks sufficient discriminative terms. In the pre-retrieval stage, using semantic smoothing during query formulation can mitigate the effect of those omissions. (2) Incorporating user negative feedback, i.e., improving a search engine by learning from user feedback in the post-retrieval stage. When a query is so difficult that all the top retrieved results (e.g., top 10) are completely irrelevant, the feedback that a user can provide is solely negative. We propose a generalized optimization framework to learn from feedback on non-relevant documents to prune extensively, but carefully, a large number of non-relevant documents from the top of the ranking list. (3) Balancing priorities when learning from user interaction in order to optimize the whole session. When presenting the search results to the user, there is a tradeoff between promoting those with the highest immediate utility and promoting those with the best potential for collecting feedback information, which can be used to better serve the user over the course of the session (interactive search stage). We frame this tradeoff as a problem of optimizing the diversification of search results, and we propose a machine learning approach that adaptively optimizes each individual user query such that we maximize the overall utility of the entire session. In summary, this dissertation is expected to advance the state of the art of search engines by providing a suite of novel search algorithms, which use the listed approaches to improve a search engine's ability to handle difficult queries. 3.9% topic match

3.9%

7.0

2018

[243] A Personalized Group-Based Recommendation Approach for Web Search in E-Learning Mohammad Mustaneer Rahman and N. A. Abdullah IEEE Access 2018 - 46 citations - Show abstract - Cite 3.9% topic match

3.9%

0.5

2017

[244] Behavior-driven optimization techniques for scalable data exploration L. Battle Journal Not Provided 2017 - 4 citations - Show abstract - Cite Abstract: Interactive visualizations are a popular medium used by scientists to explore, analyze and generally make sense of their data. However, with the overwhelming amounts of data that scientists collect from various instruments (e.g., telescopes, satellites, gene sequencers and field sensors), they need ways of efficiently transforming their data into interactive visualizations. Though a variety of visualization tools exist to help people make sense of their data, these tools often rely on database management systems (or DBMSs) for data processing and storage; and unfortunately, DBMSs fail to process the data fast enough to support a fluid, interactive visualization experience. This thesis blends optimization techniques from databases and methodology from HCI and visualization in order to support interactive and iterative exploration of large datasets. Our main goal is to reduce latency in visualization systems, i.e., the time these systems spend responding to a user’s actions. We demonstrate through a comprehensive user study that latency has a clear (negative) effect on users’ high-level analysis strategies, which becomes more pronounced as the latency is increased. Furthermore, we find that users are more susceptible to the effects of system latency when they have existing domain knowledge, a common scenario for data scientists. We then developed a visual exploration system called Sculpin that utilizes a suite of optimizations to reduce system latency. Sculpin learns user exploration patterns automatically, and exploits these patterns to pre-fetch data ahead of users as they explore. We then combine data-prefetching with incremental data processing (i.e., incremental materialization) and visualization-focused caching optimizations to further boost performance. With all three of these techniques (pre-fetching, caching, and pre-computation), Sculpin is able to: create visualizations 380% faster and respond to user interactions 88% faster than existing visualization systems, while also using less than one third of the space required by other systems to store materialized query results. Thesis Supervisor: Michael Stonebraker Title: Adjunct Professor of Electrical Engineering and Computer Science 3.9% topic match

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2016

[245] Using Crowd Sourcing to Measure the Effects of System Response Delays on User Engagement Brandon T. Taylor, ..., and A. Smailagic Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems 2016 - 8 citations - Show abstract - Cite 3.9% topic match

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2002

[246] The impact of time constraint on information search strategies in complex choice tasks M. Weenig and M. Maarleveld Journal of Economic Psychology 2002 - 82 citations - Show abstract - Cite 3.9% topic match

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2.7

2006

[247] Information foraging on the web: The effects of "acceptable" Internet delays on multi-page information search behavior A. Dennis and Nolan J. Taylor Decis. Support Syst. 2006 - 48 citations - Show abstract - Cite 3.8% topic match

3.8%

0.2

1998

[248] Optimizing recall/precision scores in IR over the WWW M. Montebello Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 1998 - 5 citations - Show abstract - Cite Abstract: The rapid growth of the World Wide Web (WWW) and the massive size of the information corpus available for access symbolizes the wealth and benefits of this medium. At the same time this immense pool of information has created an information overflow which requires users to revert to techniques and tools in order to take advantage of such a resource and enhance the effectiveness of online information access. Search engines were created to assist users to find information by employing indexing techniques and suggest appropriate alternatives to browse. These search engines have inefficiencies and are not focused enough to the needs of individual users and little has been done to ensure that the information presented is of a high recall and precision standard. ‘Recall’ measures how efficient the system is at retrieving the relevant documents from the WWW, while ‘precision’ measures the relevance of the retrieved set of documents to the users’ requirements. We present our experiences with a system we developed to optimize the recall/precision scores. We attempt to achieve this objective by employing a number of search engines and user profiling in tandem. Namely, we attempt to optimize: l recall by aggregating hits from major search engines and other previously developed retrieval systems, l precision by generating user profiles and predicting appropriate and focused information to specific users. Our system is able to easily and inexpensively accommodate future generations of web-based retrieval systems and technologies. Our contribution to the IR field is that we were able to incorporate several desirable characteristics from different techniques to optimize and personalize WWW searching. 1 Background and Motivations In recent years there has been a well-publicized explosion of information available on the Internet, and a corresponding increase in usage. The WWW’s sheer scale and its exponential growth renders the task of simply finding information, tucked away in some Web site, laborious, tedious, long-winded and time consuming. The fact that Permission to make digital/hard copy of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage, the cowriaht notice. the title of the Dublication and its date appear, and notice is gi;en that copying is by permission of ACM, Inc. To copy otherwise, to republish, to post on servers OF to redistribute to lists, requires prior specific permission and/or fee. SlGIR’98, Melbourne, Australia @ 1998 ACM l-58113-015-5 8/98 $5.00. a user’s time is valuable and that relevant information might not be accessed, imposes serious restrictions on the efficient use of the WWW and the benefits that users can expect from their interaction. Users are faced with the problem of search engines being too generalised[2, 31 and not focused enough[8, 91 to their real and specific needs. This triggered research to develop more sophisticated techniques and agent like systems that make use of the user profile to personalize the service they provide and add value to the information they presented [4, 5, 71. In Section 2 we briefly present the system we developed to reuse information generated by search engines and previously developed retrieval systems. Conceptually, it is similar to a meta-search engine, but with the major difference that it employs user profiling to specifically target documents for individual users. The system makes use of a number of machine learning techniques to extract features from documents and generate profiles. We point out how this web-based application has been designed and developed to incorporate different techniques and evolve in the eventuality that new retrieval systems or novel machine learning techniques need to be implemented in future. Finally, in Section 3 we present our conclusions and future work. 2 Architecture and Current Implementation Our goal is to achieve a high recall and high precision performance score on the information presented to the user. Recall measures how efficient the system is at retrieving the relevant documents from the WWW, while precision measures the relevance of the retrieved set of documents to the user requirements. In order to obtain a high recall execution we make use of the metasearch approach, namely, hits returned by a number of traditional search engines together with previously developed retrieval systems are blended, aggregated and utilised by our system. On the other hand, to achieve a high precision execution we employ machine learning techniques[6] to extract features from documents specific users find interesting, generate a profile and predict other documents that fit this profile. The task performed by the system is decomposed into a number of simpler tasks. Figure 1 shows the major components of the system: the WWW and the external systems at the bottom level, the underlying application software on the next level up, and the GUI at the top. All the external systems are considered to be black boxes and action is taken upon the information they output. Wrappers are used to manage the appropriate and proper handshaking between the diverse search engines and the other retrieving systems and the application layer. The system requires an administrator to manage the general needs and demands of a specific interest group of users. The administrator can initialise search terms tailored to any type of interest group and furthermore users will be able to suggest any other terms to add to the main search list. Documents relevant to the specific area of interest are retrieved and stored by the underlying application within the main index, and when a user logs-in he/she is able to benefit from the systems’ high recall fidelity. Having analysed the documents, individual users can bookmark and highlight specific items as interesting and appealing. These will be saved inside their personal database index. At this stage the underlying application plays another important role in attaining precise targeting of documents to individual users by generating a profile from the personal database index and predicting other documents from within the main index. Users can decide to add the suggested documents to their personal database index or remove them completely. As new and suggested documents are entered in the personal database index the user profile becomes more focused and finely tuned, as a result of which higher precision results will be achieved. 3.8% topic match

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2007

[249] The impact of time constraints on Internet and Web use Debra J. Slone J. Assoc. Inf. Sci. Technol. 2007 - 24 citations - Show abstract - Cite 3.8% topic match

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2006

[250] Giving way to Google G. Gorman Online Inf. Rev. 2006 - 19 citations - Show abstract - Cite 3.7% topic match

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2004

[251] An Experiment to Investigate the Externalities of Search L. Allsopp Behavioral & Experimental Economics 2004 - 0 citations - Show abstract - Cite 3.7% topic match

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2015

[252] The Effects of Repeated Testing, Simulated Malingering, and Traumatic Brain Injury on High-Precision Measures of Simple Visual Reaction Time D. Woods, ..., and Maria Gardani Frontiers in Human Neuroscience 2015 - 17 citations - Show abstract - Cite Abstract: Simple reaction time (SRT), the latency to respond to a stimulus, has been widely used as a basic measure of processing speed. In the current experiments, we examined clinically-relevant properties of a new SRT test that presents visual stimuli to the left or right hemifield at varying stimulus onset asynchronies (SOAs). Experiment 1 examined test-retest reliability in 48 participants who underwent three test sessions at weekly intervals. In the first test, log-transformed (log-SRT) z-scores, corrected for the influence of age and computer-use, were well predicted by regression functions derived from a normative population of 189 control participants. Test-retest reliability of log-SRT z-scores was measured with an intraclass correlation coefficient (ICC = 0.83) and equaled or exceeded those of other SRT tests and other widely used tests of processing speed that are administered manually. No significant learning effects were observed across test sessions. Experiment 2 investigated the same participants when instructed to malinger during a fourth testing session: 94% showed abnormal log-SRT z-scores, with 83% producing log-SRT z-scores exceeding a cutoff of 3.0, a degree of abnormality never seen in full-effort conditions. Thus, a log-SRT z-score cutoff of 3.0 had a sensitivity (83%) and specificity (100%) that equaled or exceeded that of existing symptom validity tests. We argue that even expert malingerers, fully informed of the malingering-detection metric, would be unable to successfully feign impairments on the SRT test because of the precise control of SRT latencies that would be required. Experiment 3 investigated 26 patients with traumatic brain injury (TBI) tested more than 1 year post-injury. The 22 patients with mild TBI showed insignificantly faster SRTs than controls, but a small group of four patients with severe TBI showed slowed SRTs. Simple visual reaction time is a reliable measure of processing speed that is sensitive to the effects of malingering and TBI. 3.7% topic match

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2009

[253] Delays and User Performance in Human-Computer-Network Interaction Tasks B. Caldwell and Enlie Wang Human Factors: The Journal of Human Factors and Ergonomics Society 2009 - 17 citations - Show abstract - Cite 3.6% topic match

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2015

[254] Personalization of Search Engines, Based-on Comparative Analysis of User Behavior Bostan Shekoofe and Ghasemzadeh Mohammad Journal of Advances in Computer Research 2015 - 3 citations - Show abstract - Cite 3.5% topic match

3.4%

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2005

[255] Genetic Programming-Based Discovery of Ranking Functions for Effective Web Search Weiguo Fan, ..., and Praveen Pathak Journal of Management Information Systems 2005 - 93 citations - Show abstract - Cite 3.4% topic match

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2014

[256] Google Scholar and Its Competitors: Accessing Scholarly Resources on the Web Ingrid Hsieh-Yee Journal Not Provided 2014 - 0 citations - Show abstract - Cite 3.4% topic match

3.4%

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1996

[257] Towards an Empirical Model of WWW Site Response Times T. A. Wagner Journal Not Provided 1996 - 0 citations - Show abstract - Cite 3.4% topic match

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2012

[258] Graduated locality-aware scheduling for search engines Jaimie Kelley and Christopher Stewart International Middleware Conference 2012 - 0 citations - Show abstract - Cite 3.4% topic match

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2007

[259] User-Schedule-based Web Page Recommendation Tetsuyake Oishi, ..., and Miyuki Koshimura IEEE/WIC/ACM International Conference on Web Intelligence (WI'07) 2007 - 1 citations - Show abstract - Cite 3.4% topic match

3.3%

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2012

[260] Joint Optimization of Index Freshness and Coverage in Real-Time Search Engines Yongwook Shin, ..., and Jonghun Park IEEE Transactions on Knowledge and Data Engineering 2012 - 11 citations - Show abstract - Cite 3.3% topic match

3.2%

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2013

[261] Load-sensitive selective pruning for distributed search Daniele Broccolo, ..., and N. Tonellotto Proceedings of the 22nd ACM international conference on Information & Knowledge Management 2013 - 20 citations - Show abstract - Cite 3.2% topic match

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2008

[262] The perspectives of improving Web search engine quality J. Chen, ..., and Yao-Sheng Chang https://doi.org/10.4018/978-1-59904-847-5.CH027 2008 - 5 citations - Show abstract - Cite 3.2% topic match

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2003

[263] Google in perspective: understanding and enhancing student search skills Nancy J. Becker New Review of Academic Librarianship 2003 - 46 citations - Show abstract - Cite 3.1% topic match

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2013

[264] Choices in batch information retrieval evaluation Falk Scholer, ..., and Paul Thomas Australasian Document Computing Symposium 2013 - 3 citations - Show abstract - Cite 3.1% topic match

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15.4

2008

[265] Performance of compressed inverted list caching in search engines Jiangong Zhang, ..., and Torsten Suel The Web Conference 2008 - 250 citations - Show abstract - Cite 3.0% topic match

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2019

[266] TempWeb 2019 Chairs' Welcome M. Spaniol, ..., and J. Masanès Companion Proceedings of The 2019 World Wide Web Conference 2019 - 0 citations - Show abstract - Cite 3.0% topic match

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2010

[267] Response time of BPEL4WS constructors S. Haddad, ..., and S. Youcef The IEEE symposium on Computers and Communications 2010 - 9 citations - Show abstract - Cite 3.0% topic match

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2014

[268] NTCIR temporalia: a test collection for temporal information access research Hideo Joho, ..., and Roi Blanco Proceedings of the 23rd International Conference on World Wide Web 2014 - 33 citations - Show abstract - Cite 3.0% topic match

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2003

[269] Waiting online: a review and research agenda Gerard Ryan and M. Valverde Internet Res. 2003 - 39 citations - Show abstract - Cite 3.0% topic match

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2009

[270] Making the road by searching - A search engine based on Swarm Information Foraging Daniel Gayo-Avello and David J. Brenes ArXiv 2009 - 6 citations - Show abstract - Cite - PDF 2.9% topic match

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2021

[271] On Latency of E-Commerce Platforms Marcus Basalla, ..., and J. Brocke Journal of Organizational Computing and Electronic Commerce 2021 - 7 citations - Show abstract - Cite 2.9% topic match

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2001

[272] Brewsing the web: delay, determination and satisfaction F. Culwin and X. Faulkner Proceedings of the 34th Annual Hawaii International Conference on System Sciences 2001 - 9 citations - Show abstract - Cite 2.8% topic match

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2001

[273] Vector-space ranking with effective early termination V. Anh, ..., and Alistair Moffat Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2001 - 221 citations - Show abstract - Cite 2.8% topic match

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2012

[274] Random selection assisted long web search query optimization Teng-Sheng Moh and Jehaan Irani https://doi.org/10.1145/2184512.2184555 2012 - 3 citations - Show abstract - Cite 2.8% topic match

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2006

[275] Google Scholar: A Tool to Search Scholarly Information on the Web B. Maharana and Sasmitā Mahāpātra Journal Not Provided 2006 - 2 citations - Show abstract - Cite 2.8% topic match

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2017

[276] Query Driven Algorithm Selection in Early Stage Retrieval J. Mackenzie, ..., and Jimmy J. Lin Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining 2017 - 47 citations - Show abstract - Cite - PDF 2.7% topic match

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2003

[277] When the Wait Isn ’ t So Bad : The Interacting Effects of Web Site Speed , Familiarity , and Breadth January 31 , 2003 D. Galletta Journal Not Provided 2003 - 6 citations - Show abstract - Cite 2.7% topic match

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6.3

2010

[278] Exploring reductions for long web queries Niranjan Balasubramanian, ..., and Vitor R. Carvalho Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval 2010 - 88 citations - Show abstract - Cite 2.5% topic match

2.4%

0.0

2014

[279] Query Scheduling Techniques and Power/Latency Trade-off Model for Large-Scale Search Engines Ana Maŕıa Freire Veiga Journal Not Provided 2014 - 0 citations - Show abstract - Cite 2.4% topic match

2.3%

0.5

2018

[280] Facilitating Exploration with Interaction Snapshots under High Latency Yifan Wu, ..., and Eugene Wu 2020 IEEE Visualization Conference (VIS) 2018 - 3 citations - Show abstract - Cite 2.3% topic match

2.3%

1.5

2010

[281] Analysis of Markov model on different web Prefetching and caching schemes B. Nigam and Suresh Jain 2010 IEEE International Conference on Computational Intelligence and Computing Research 2010 - 21 citations - Show abstract - Cite 2.3% topic match

2.3%

0.6

1998

[282] BENEFITS OF TRANSPARENT CONTENT NEGOTIATION IN HTTP S. Seshan, ..., and R. Katz Journal Not Provided 1998 - 16 citations - Show abstract - Cite 2.3% topic match

2.3%

0.1

2003

[283] THE EFFECTS OF SCANNABILITY ON INFORMATION SEARCH: AN ONLINE EXPERIMENT. Michael D. Kickmeier and D. Albert Journal Not Provided 2003 - 2 citations - Show abstract - Cite 2.3% topic match

2.2%

1.3

1999

[284] Improving response time by search pruning in a content-based image retrieval system, using inverted file techniques McG.D. Squire, ..., and Wolfgang Müller Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries (CBAIVL'99) 1999 - 32 citations - Show abstract - Cite 2.2% topic match

2.1%

3.9

2018

[285] Use of Web search engines and personalisation in information searching for educational purposes Sara Salehi, ..., and H. Ashman Inf. Res. 2018 - 24 citations - Show abstract - Cite 2.1% topic match

2.0%

1.5

2013

[286] Understanding the privacy-personalization dilemma for web search: a user perspective S. Panjwani, ..., and Sharad Jaiswal Proceedings of the SIGCHI Conference on Human Factors in Computing Systems 2013 - 17 citations - Show abstract - Cite 2.0% topic match

2.0%

0.5

1979

[287] Implementation of Response Latency Measures J. Maclachlan, ..., and P. Labarbera Journal of Marketing Research 1979 - 24 citations - Show abstract - Cite 2.0% topic match

2.0%

0.0

2015

[288] Standing on the shoulders of the Google giant: Sustainable discovery and Google Scholar’s comprehensive coverage. Max Kemman Journal Not Provided 2015 - 0 citations - Show abstract - Cite 2.0% topic match

1.9%

1.2

2018

[289] A Study of Immediate Requery Behavior in Search Haotian Zhang, ..., and Mark D. Smucker Proceedings of the 2018 Conference on Human Information Interaction & Retrieval 2018 - 8 citations - Show abstract - Cite 1.9% topic match

1.9%

0.8

2022

[290] Sequence Learning in an Online Serial Reaction Time Task: The Effect of Task Instructions Jaskanwaljeet Kaur and R. Balasubramaniam Journal of Motor Learning and Development 2022 - 2 citations - Show abstract - Cite 1.9% topic match

1.8%

5.1

2011

[291] Evaluation strategies for top-k queries over memory-resident inverted indexes M. Fontoura, ..., and Jason Y. Zien Proceedings of the VLDB Endowment 2011 - 66 citations - Show abstract - Cite 1.8% topic match

1.8%

None

[292] 6 Personalized Search on the World Wide Web A. Micarelli, ..., and S. Gauch Journal Not Provided None - 0 citations - Show abstract - Cite 1.8% topic match

1.7%

0.0

1997

[293] A Study of Information Searching Behaviors Caused by Time pressure of Researchers Hong Ki-Churl Journal of The Korean Society for Library and Information Science 1997 - 0 citations - Show abstract - Cite 1.7% topic match

1.7%

0.7

2016

[294] AccuracyTrader: Accuracy-Aware Approximate Processing for Low Tail Latency and High Result Accuracy in Cloud Online Services Rui Han, ..., and Jianfeng Zhan 2016 45th International Conference on Parallel Processing (ICPP) 2016 - 6 citations - Show abstract - Cite - PDF 1.7% topic match

1.5%

1.6

2018

[295] Google Scholar: the 'big data' bibliographic tool Emilio Delgado López-Cózar, ..., and J. M. Ayllón ArXiv 2018 - 10 citations - Show abstract - Cite - PDF 1.5% topic match

1.5%

0.0

2019

[296] Efficient Top-k Document Retrieval Antonio Mallia Journal Not Provided 2019 - 0 citations - Show abstract - Cite 1.5% topic match

1.5%

1.1

1996

[297] How machine delays change user strategies P. O'Donnell and S. Draper ACM SIGCHI Bull. 1996 - 32 citations - Show abstract - Cite 1.5% topic match

1.5%

0.5

2018

[298] An experimental investigation of the secretary problem : factors affecting sequential search behaviour. Y. Hsiao Journal Not Provided 2018 - 3 citations - Show abstract - Cite 1.5% topic match

1.5%

0.0

1988

[299] Users' perception of computer system response time R. Geist and R. A. Allen Journal Not Provided 1988 - 0 citations - Show abstract - Cite 1.5% topic match

1.4%

0.6

2014

[300] Reward effects on sequential action learning in a trajectory serial reaction time task George Kachergis, ..., and B. Hommel 4th International Conference on Development and Learning and on Epigenetic Robotics 2014 - 6 citations - Show abstract - Cite 1.4% topic match

1.4%

2.4

2021

[301] Enhancing the Interactivity of Dataframe Queries by Leveraging Think Time Doris Xin, ..., and Aditya G. Parameswaran IEEE Data Eng. Bull. 2021 - 8 citations - Show abstract - Cite - PDF 1.4% topic match

1.4%

1.3

2008

[302] Students' Attitudes Towards Web Search Engines – Increasing Appreciation of Sophisticated Search Strategies Konstantina Martzoukou https://doi.org/10.1515/libr.2008.019 2008 - 22 citations - Show abstract - Cite 1.4% topic match

1.4%

2.6

2005

[303] Using top-ranking sentences to facilitate effective information access Ryen W. White, ..., and I. Ruthven J. Assoc. Inf. Sci. Technol. 2005 - 49 citations - Show abstract - Cite 1.4% topic match

1.3%

0.0

2011

[304] Efficient and effective solutions for search engines Xiangfei Jia Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval 2011 - 0 citations - Show abstract - Cite 1.3% topic match

1.3%

2.6

2015

[305] goDiscovery: Web Service Discovery Made Efficient Yehia Elshater, ..., and Patrick Martin 2015 IEEE International Conference on Web Services 2015 - 24 citations - Show abstract - Cite 1.3% topic match

1.2%

2.6

2013

[306] Supporting exploratory people search: a study of factor transparency and user control Shuguang Han, ..., and Zhen Yue Proceedings of the 22nd ACM international conference on Information & Knowledge Management 2013 - 28 citations - Show abstract - Cite 1.2% topic match

1.2%

0.7

2009

[307] Effects of Time Pressure on the Use of an Automated Decision Support System for Strike Planning M. Cummings, ..., and Jonathan C. Las Fargeas Journal Not Provided 2009 - 11 citations - Show abstract - Cite 1.2% topic match

1.1%

0.8

2009

[308] Google Scholar ’ s Ranking Algorithm : The Impact of Articles ’ Age ( An Empirical Study ) Otto-von-Guericke Journal Not Provided 2009 - 13 citations - Show abstract - Cite 1.1% topic match

1.1%

30.3

2004

[309] A study on tolerable waiting time: how long are Web users willing to wait? F. Nah Behaviour & Information Technology 2004 - 614 citations - Show abstract - Cite 1.1% topic match

1.1%

0.2

2005

[310] SAVVY SEARCHING Google Scholar : the pros and the cons P. Jacsó Journal Not Provided 2005 - 4 citations - Show abstract - Cite 1.1% topic match

1.0%

0.0

2011

[311] Title: Personal Web Search in the Age of Semantic Capitalism Subtitle: Diagnosing the Mechanisms of Personalisation F. Stalder Journal Not Provided 2011 - 0 citations - Show abstract - Cite 1.0% topic match

1.0%

3.9

2009

[312] Google Scholar como herramienta para la evaluación científica. D. Torres-Salinas, ..., and Emilio Delgado López-Cózar Profesional De La Informacion 2009 - 59 citations - Show abstract - Cite 1.0% topic match

0.9%

17.8

2012

[313] Time-based calibration of effectiveness measures Mark D. Smucker and C. Clarke Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2012 - 213 citations - Show abstract - Cite 0.9% topic match

0.9%

0.0

2010

[314] On the credibility of search engines in network bandwidth Xu Bai, ..., and Chunyan Han 2010 2nd International Conference on Signal Processing Systems 2010 - 0 citations - Show abstract - Cite 0.9% topic match

0.9%

0.1

2011

[315] Personalizing information retrieval using task stage, topic knowledge, and task products Jingjing Liu SIGIR Forum 2011 - 1 citations - Show abstract - Cite Abstract: Personalization of information retrieval tailors search towards individual users to meet their particular information needs by taking into account information about users and their contexts, often through implicit sources of evidence such as user behaviors and contextual factors. The current study looks particularly at users' dwelling behavior, measured by the time that they spend on documents; and several contextual factors: the stage of users' work tasks, task type, users' knowledge of task topics, to explore whether or not taking account of task stage, task type, and topic knowledge could help predict document usefulness from the time that users spend on the documents. This study also investigates whether or not expanding queries with important terms extracted from task products and useful pages improves search performance. To these ends, a controlled lab experiment was conducted with 24 student participants, each coming three times in a two-week period to work on three subtasks in a general work task. Data were collected by logging software that recorded user-system interaction and questionnaires that elicited users' background information and perceptions on a number of aspects. Observations in the study and examinations of the data found that the time users spent on documents could have three different types: total display time, total dwell time, and decision time, which had different roles in working as a reliable indicator of document usefulness. Task stage was found to help interpret certain types of time as reliable indicators of document usefulness in certain task types, so was topic knowledge, and the latter played a more significant role when both were available. This study contributes to a better understanding of how information seeking behaviors, specifically, time that users spend on documents, can be used as implicit evidence of document usefulness, as well as how contextual factors of task stage, topic knowledge, and task type can help interpret time as an indicator of usefulness. These findings have theoretical and practical implications for using behaviors and contextual factors in the development of personalization systems. Future studies are suggested on making use of these findings as well as research on related issues. 0.9% topic match

0.9%

1.8

1995

[316] Time affordances: the time factor in diagnostic usability heuristics A. Conn International Conference on Human Factors in Computing Systems 1995 - 52 citations - Show abstract - Cite 0.9% topic match

0.9%

2.5

2016

[317] Measuring and Predicting Search Engine Users’ Satisfaction Ovidiu Dan and Brian D. Davison ACM Computing Surveys (CSUR) 2016 - 20 citations - Show abstract - Cite 0.9% topic match

0.9%

0.5

2001

[318] Measuring the mean Web page size and its compression to limit latency and improve download time P. Destounis, ..., and John Tzimas Internet Res. 2001 - 11 citations - Show abstract - Cite 0.9% topic match

0.9%

0.3

2021

[319] Analysis Of Pattern Matching Algorithms Used For Searching The Patterns In MLIR Framework G. S. Kumar and Manjunath Swamy https://doi.org/10.17762/TURCOMAT.V12I7.2646 2021 - 1 citations - Show abstract - Cite 0.9% topic match

0.9%

0.7

2013

[320] Imperfect tools: Google Scholar vs. traditional commercial library databases J. Arendt against the grain 2013 - 8 citations - Show abstract - Cite Abstract: Like every other resource that a library might offer, Google Scholar has strengths and limitations. Instead of rejecting Google Scholar because it does not do everything that the library or librarians do, Google Scholar should be accepted or rejected based on how well it assists in a particular step in information seeking. That step traditionally has been assisted by indexing and abstracting resources. In some circumstances Google Scholar is a better tool than the indexing and abstracting resources; in other circumstances it is not. This article examines the strengths and weaknesses of Google Scholar compared to subscription indexing and abstracting databases. It critiques college and university libraries’ continued use of subscription databases that fail to provide a clear advantage over Google Scholar. When Google Scholar was introduced, it initially met with some praise and a fair amount of criticism from the library world. Both the praise and criticism generally were deserved. Unfortunately, early responses sometimes compared Google Scholar to the library as a whole or to an idealized vision of library databases rather than to the real, imperfect indexing and abstracting databases offered through the library. Some of the faults that early commentators found in Google Scholar included lack of a controlled vocabulary, lack of authority control, incomplete or uneven coverage depending on discipline, and time lags between publication and appearance in the database. These same faults could be pointed out for Web of Science, a venerable subscription database. Another criticism of Google Scholar was that its definition of “scholarly” includes materials that have not undergone peer review, so it may lead users to this unvetted material. Again, this criticism also could be leveled against a subscription database. For example, book reviews, editorials and commentaries regularly appear in search results from Academic Search Premier, even when the search is limited to scholarly (peer reviewed) journals. Instead of comparing Google Scholar to the ideal resource, a fairer comparison would be to actual subscription databases. Some evaluations have explored whether a subscription database produces better results than Google Scholar. When librarians conduct test searches using advanced search features in library databases, they get somewhat better results with the database than with Google Scholar. When college students conduct the searches, the advantage for the subscription database evaporates. The sources students find from Google Scholar are as good as or better than those found through the library’s databases. For these novice users, often subscription databases do not provide a clear advantage over Google Scholar. Librarians may be able to use controlled vocabularies to produce more precise results from a database than from Google Scholar or to find special materials that could not be found through Google Scholar, but library patrons are not librarians. Simply having a controlled vocabulary or special materials is not good enough for a novice user. If users cannot figure out the controlled vocabulary or find the special materials, they cannot experience these supposed advantages. For there to be a clear advantage of a subscription database over Google Scholar, novice users should be able to complete their work more easily with the subscription database than they can with Google Scholar. Many subscription databases provide a clear advantage by simplifying access to special materials or by leveraging their controlled vocabularies. The interface designs that highlight subject terms next to results sets, such as those in EBSCOhost and Engineering Village, should be commended for their effort to guide novices to controlled vocabularies without interrupting users’ searches. Some databases and interfaces simplify users’ work in other ways. For example, Web of Knowledge provides citation assistance through EndNote Web, and full-text resources like JSTOR provide easy access to complete documents. 0.9% topic match

0.9%

0.1

2010

[321] The influence of reasoning capacity and time pressure on the information search pattern of career decision making Dawei Wang and K. Shi 2010 IEEE 2nd Symposium on Web Society 2010 - 1 citations - Show abstract - Cite 0.9% topic match

0.9%

0.5

2011

[322] Undergraduate students’ information search practices K. Nikolopoulou and V. Gialamas Journal Not Provided 2011 - 7 citations - Show abstract - Cite 0.9% topic match

0.9%

13.6

2017

[323] How Progressive Visualizations Affect Exploratory Analysis Emanuel Zgraggen, ..., and Tim Kraska IEEE Transactions on Visualization and Computer Graphics 2017 - 95 citations - Show abstract - Cite 0.9% topic match

0.8%

5.1

2008

[324] Searching for a better deal - on the influence of group decision making, time pressure and gender in a search experiment M. Ibáñez, ..., and Matthias Sutter Journal Not Provided 2008 - 83 citations - Show abstract - Cite 0.8% topic match

0.8%

0.0

2013

[325] The transference of visual scanning behaviour between two unrelated tasks : Measuring the characteristics of carry-over C. Thompson, ..., and P. Hills https://doi.org/10.1037/e636952013-550 2013 - 0 citations - Show abstract - Cite 0.8% topic match

0.7%

0.3

2015

[326] Does Google Scholar help or hurt institutional repositories Peace Ossom Williamson and Rafia Mirza Journal Not Provided 2015 - 3 citations - Show abstract - Cite 0.7% topic match

0.7%

0.0

2002

[327] Download time and intent to use a web page: Working paper series--02-09 Richard Lucy and Craig A. VanLengen Journal Not Provided 2002 - 0 citations - Show abstract - Cite 0.7% topic match

0.7%

3.7

2004

[328] On the temporal dimension of search Philip S. Yu, ..., and B. Liu https://doi.org/10.1145/1013367.1013519 2004 - 74 citations - Show abstract - Cite 0.7% topic match

0.7%

0.2

2015

[329] Aplicaciones métricas de Google Scholar para la evaluación del impacto científico Enrique Orduña-Malea, ..., and Emilio Delgado López-Cózar Journal Not Provided 2015 - 2 citations - Show abstract - Cite 0.7% topic match

0.6%

0.7

2009

[330] Google Scholar and the Continuing Education Literature Jared L. Howland, ..., and Cody W. Dickson The Journal of Continuing Higher Education 2009 - 10 citations - Show abstract - Cite 0.6% topic match

0.6%

0.0

2023

[331] Choice Deferral and Search Fatigue∗ Z. Ning Journal Not Provided 2023 - 0 citations - Show abstract - Cite 0.6% topic match

0.6%

0.0

2023

[332] Validation of cognitive and neuromuscular latencies using an iPad-based tool to quantify simple reaction times in asymptomatic subjects R. Hallgren and John Rogers Journal of Osteopathic Medicine 2023 - 0 citations - Show abstract - Cite Abstract: Abstract Context Simple reaction times (SRTs), measured in milliseconds (msec), are equal to the sum of subject-dependent latencies that occur during cognitive processing and neuromuscular responses to a preprogrammed stimulus presented to a subject. SRTs have the advantage of being a relatively pure, neurologically driven motor/sensory task that provides a clinician with a generalized assessment of functional deficits. SRTs are easily obtained, and studies have reported that the average number of finger-taps during a 10-s interval can be utilized to distinguish between patients with mild traumatic brain injury (TBI) and healthy controls. Objectives A stimulus/response SRT protocol, utilizing an iPad-based tool, was utilized to see if differentiation and quantification of individual components of cognitive latency (CL) and neuromuscular latency (NL) from a subject’s total SRT could be accomplished. The study hypothesis was that cognitive latencies, related to a specific cognitive challenge, would remain constant even as latencies due to neuromuscular challenges changed. Methods Two categories of SRT tests were utilized. The first was a simple finger tapping test (FTT), without any cognitive involvement, that was designed to quantify pure NL. The second test was a choice test (CT) that was characterized by the addition of a cognitive task to the FTT. The objective of the FTT was for the subject to simply tap a single target positioned on the iPad screen as fast as possible over an interval of 10 s. Measurement of the SRT began when a target was displayed and ended when the target was tapped. Two levels of challenge for the FTT and CT tests were presented to the subjects: a small random displacement (SD) of the target of up to 6 mm, and a large random displacement (LD) of the target of up to 24 mm. It was expected that the magnitude of the SRT (FTT) would be directly related to the magnitude of the displacement of the target due to kinematic response characteristics of the hand and arm. Results To validate the study hypothesis, CL for both small and large displacements was calculated by subtracting the respective NL from the total SRT. Utilizing a repeated measures t-test analysis utilizing SPSS, a significant difference between CL (SD) and CL (LD) at p=0.696 was not observed. Conclusions Differentiation and quantification of individual components of CL and NL from a subject’s total SRT was accomplished. An Apple iPad Pro was selected as a platform for our study because: (1) it is readily available, affordable, and programmable; (2) it meets a requirement for portability; and (3) it allows the modification/addition of test parameters to meet future needs. The ability to quantify the extent of cognitive and neuromuscular dysfunction in the TBI patient is an essential component of developing an effective treatment plan. 0.6% topic match

0.6%

1.1

2011

[333] An Analysis of User Strategies for Examining and Processing Ranked Lists of Documents Mark D. Smucker Journal Not Provided 2011 - 15 citations - Show abstract - Cite 0.6% topic match

0.6%

0.1

2007

[334] Between the Book Covers Gail M. Golderman and B. Connolly Journal of Internet Cataloging 2007 - 1 citations - Show abstract - Cite 0.6% topic match

0.6%

2.8

2012

[335] Using Google Scholar for journal impact factors and the h‐index in nationwide publishing assessments in academia – siren songs and air‐raid sirens P. Jacsó Online Information Review 2012 - 34 citations - Show abstract - Cite 0.6% topic match

0.6%

17.1

2005

[336] Student Searching Behavior and the Web: Use of Academic Resources and Google Jillian R. Griffiths and P. Brophy Libr. Trends 2005 - 330 citations - Show abstract - Cite 0.6% topic match

0.6%

1.7

1986

[337] System response time effects on user productivity Gale Martin and K. Corl Behaviour & Information Technology 1986 - 65 citations - Show abstract - Cite 0.6% topic match

0.6%

0.5

2010

[338] The Simplest Chronoscope II: Reaction Time Measured by Meterstick versus Machine A. Montare Perceptual and Motor Skills 2010 - 7 citations - Show abstract - Cite 0.6% topic match

0.6%

2.6

2018

[339] Playing with Delay: With Poor Timing Comes Poor Performance, and Experience Follows Suit Ragnhild Eg, ..., and Mark Claypool 2018 Tenth International Conference on Quality of Multimedia Experience (QoMEX) 2018 - 16 citations - Show abstract - Cite 0.6% topic match

0.5%

1.8

2009

[340] Google Scholar's Ranking Algorithm: The Impact of Articles' Age (An Empirical Study) Jöran Beel and Bela Gipp 2009 Sixth International Conference on Information Technology: New Generations 2009 - 28 citations - Show abstract - Cite 0.5% topic match

0.5%

0.0

2018

[341] An Approach for Generating Query Facets using Knowledge Bases Guided B.Divya and -Prof.D.Anji Reddy Journal Not Provided 2018 - 0 citations - Show abstract - Cite 0.5% topic match

0.5%

4.4

2014

[342] Graduate students appreciate Google Scholar, but still find use for libraries Ming-der Wu and Shih-chuan Chen Electron. Libr. 2014 - 44 citations - Show abstract - Cite 0.5% topic match

0.5%

0.0

2018

[343] U-Multirank: Data analytics and scientometrics Frans Kaiser and N. Zeeman https://doi.org/10.1201/9781315155890-11 2018 - 0 citations - Show abstract - Cite 0.5% topic match

0.4%

0.2

2015

[344] Affective Signals as Implicit Indicators of Information Relevancy and Information Processing Strategies Roberto I. González-Ibáñez Journal Not Provided 2015 - 2 citations - Show abstract - Cite Abstract: Online search has become a common behavior among computer users with millions using search engines to find and access information on the Internet. In the last fifteen years, search engines have evolved progressively from keyword-based indexing methods to the development of sophisticated ranking algorithms that use relevance feedback (Lv & Zhai, 2010), query expansion (Xu & Croft, 1996), and various forms of user modeling to provide personalization (Liu & Belkin, 2010) and recommendation. Some of the recent approaches have also started incorporating social signals (e.g. (Carmel, Roitman, & Yom-Tov, 2010)). As part of this evolution process, the interaction between users and search engines have also changed through the incorporation of alternative methods that searchers can use to represent their information needs (e.g. queries based on text, audio, images, and video) and visualize the results retrieved by search engines. From a system perspective, it is expected that such improvements are able to fulfill the information needs of their users and at the same time improve their experience. Yet, systems fail in determining how their users process information and what they feel during their search processes. In the absence of explicit relevance judgments, search engines rely on implicit measures such as dwell time in Web pages (Fox, Karnawat, Mydland, Dumais, & White, 2005) and click-through behaviors (Hassan, Jones, & Klinkner, 2010) to infer what pages are likely to be relevant to users, which in turn can provide feedback to systems that can be later incorporated in the ranking process. To fill in this gap in the interaction between users and search engines, there have been attempts to explore hidden cognitive and affective aspects. Some have investigated eye movements (Gwizdka & Cole, 2013), brain activity (Moshfeghi, Pinto, Pollick, & Jose, 2013), facial expressions (FE) (Arapakis, Jose, & Gray, 2008; González-Ibáñez, Shah, & Córdova-Rubio, 2011; Lopatovska, 2011; Moshfeghi & Jose, 2013), and Gonzalez-Ibañez, Roberto I; Shah, Chirag. (2015). Affective Signals as Implicit Indicators of Information Relevancy and Information Processing Strategies. In iConference 2015 Proceedings. 0.4% topic match

0.4%

None

[345] Googling the news Opportunities and challenges in studying news events through Google Search No author found Journal Not Provided None - 0 citations - Show abstract - Cite 0.4% topic match

0.4%

8.2

2011

[346] Personal Web searching in the age of semantic capitalism: Diagnosing the mechanisms of personalisation M. Feuz, ..., and F. Stalder First Monday 2011 - 110 citations - Show abstract - Cite 0.4% topic match

0.4%

2.7

2012

[347] Toward task-dependent evaluation of web-QoE: Free exploration vs. “Who Ate What?” D. Strohmeier, ..., and A. Raake 2012 IEEE Globecom Workshops 2012 - 31 citations - Show abstract - Cite 0.4% topic match

0.4%

None

[348] Googling the news Opportunities and challenges in studying news events through Google Search Ørmen Journal Not Provided None - 3 citations - Show abstract - Cite 0.4% topic match

0.4%

1.6

2017

[349] An Investigation of the Effect of Latency on the Operator’s Trust and Performance for Manual Multi-robot Teleoperated Tasks Hunter Rogers, ..., and K. Madathil Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2017 - 11 citations - Show abstract - Cite 0.4% topic match

0.4%

0.6

2019

[350] Time Pressure and Regret in Sequential Search F. Klimm, ..., and S. Schudy CESifo: Behavioural Economics (Topic) 2019 - 3 citations - Show abstract - Cite 0.4% topic match

0.4%

0.0

2007

[351] Currency of Search: Time Versus Money Ritesh Saini and A. Monga ACR North American Advances 2007 - 0 citations - Show abstract - Cite 0.4% topic match

0.4%

0.1

2017

[352] Incremental Information Retrieval. Finding new information by registering and ignoring already seen search results Erlend Johannessen Journal Not Provided 2017 - 1 citations - Show abstract - Cite 0.4% topic match

0.4%

2.6

2015

[353] An Analysis of Theories of Search and Search Behavior L. Azzopardi and G. Zuccon Proceedings of the 2015 International Conference on The Theory of Information Retrieval 2015 - 23 citations - Show abstract - Cite 0.4% topic match

0.4%

2.2

2011

[354] Timestamp-based result cache invalidation for web search engines Sadiye Alici, ..., and Ö. Ulusoy Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval 2011 - 29 citations - Show abstract - Cite 0.4% topic match

0.3%

0.0

2002

[355] Analysis of Search Engine Use, Search Behaviors and Aptitude by Web Users Hae-young Rieh Journal of The Korean Society for Library and Information Science 2002 - 1 citations - Show abstract - Cite 0.3% topic match

0.3%

0.0

2013

[356] Improving web user experience with caching user interface A. Opalinski https://doi.org/10.21936/SI2013_V34.N2B.62 2013 - 0 citations - Show abstract - Cite 0.3% topic match

0.3%

2.7

2005

[357] Running with the Devil R. Donlan and R. Cooke Internet Reference Services Quarterly 2005 - 52 citations - Show abstract - Cite 0.3% topic match

0.3%

0.8

2009

[358] Take your time first, time your search later: How college students perceive time in Web searching Shu-Yi Chen and Soo Young Rieh ASIS&T Annual Meeting 2009 - 12 citations - Show abstract - Cite 0.3% topic match

0.3%

1.6

2014

[359] Latency Thresholds for Usability in Games: A Survey K. Raaen and Tor-Morten Grønli Norsk Informatikkonferanse 2014 - 17 citations - Show abstract - Cite 0.3% topic match

0.3%

0.5

2008

[360] Using Google Scholar at the Reference Desk Karen Bronshteyn and Kathryn Tvaruzka Journal of Library Administration 2008 - 8 citations - Show abstract - Cite 0.3% topic match

0.3%

0.0

2022

[361] The Effect of Time Pressure on Static and Dynamic Visual Search Performance Based on Eye Movement Data Xinyi Sui, ..., and Yan Li 2022 12th International Conference on Information Technology in Medicine and Education (ITME)v 2022 - 0 citations - Show abstract - Cite 0.3% topic match

0.3%

1.2

2005

[362] Using top-ranking sentences to facilitate effective information access: Book Reviews Ryen W. White, ..., and I. Ruthven Journal of the Association for Information Science and Technology 2005 - 23 citations - Show abstract - Cite 0.3% topic match

0.3%

0.1

2013

[363] Stimulus-response tests: An applied demonstration: Demonstration paper C. Matheus and Justin Svegliato IEEE 7th International Conference on Research Challenges in Information Science (RCIS) 2013 - 1 citations - Show abstract - Cite 0.3% topic match

0.3%

3.7

2009

[364] Compressing term positions in web indexes Hao Yan, ..., and Torsten Suel Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval 2009 - 56 citations - Show abstract - Cite 0.3% topic match

0.3%

0.0

1997

[365] The role of time orientation in decision-making under time pressure / D. Dickter Journal Not Provided 1997 - 0 citations - Show abstract - Cite Abstract: This study examined the role of time orientation on decision-making under time pressure. Time orientation, an individual difference in the attention to time and the perception that time is a scarce resource, was hypothesized to exaggerate individuals' behavioral reactions to time-pressured decisions. In addition, the study examined time orientation's influence on perceived time pressure, the presumed impetus for time-saving strategies when decision-making time is limited. Given the same objective cues, it was argued, time-oriented individuals may have a heightened perception of time pressure and may have a greater propensity to use decision heuristics designed to conserve time. Participants completed an electronic information board monitoring information search during a series of decision tasks. The study included two within-subjects variables, information and time availability, with three levels each, as well as measures of time orientation. Type A behavior, state anxiety and questions about participants' experiences of time pressure during the experiment. Several research questions were addressed in this study: First, this research replicated some of the findings in the literature regarding decision search behaviors under time pressure. The study examined the main effects of two objective conditions, the amount of information presented and the time allotted, on time-conserving strategies during decision-making. An information x time interaction was hypothesized, with time conservation becoming increasingly more pronounced as the level of time availability decreased and the level of information availability increased. Time conservation was measured in a variety of ways, including searching a smaller proportion of the available information and examining variable amounts of information per option. Interactions were significant for most of the dependent variables studied. Third, it was argued that individuals with higher levels of time orientation would perceive more time pressure than those with lower levels, and would report higher stress levels. This hypothesis received mild support. Relatedly, it was argued that high time- 0.3% topic match

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1.9

2018

[366] QWERTY: The Effects of Typing on Web Search Behavior Kevin Ong, ..., and Falk Scholer Proceedings of the 2018 Conference on Human Information Interaction & Retrieval 2018 - 12 citations - Show abstract - Cite 0.3% topic match

0.2%

None

[367] Chapter 1 Introduction 1.1 Motivation No author found Journal Not Provided None - 0 citations - Show abstract - Cite Abstract: Introduced a little over a decade ago, the WorldWide Web has transformed much of the world's economy and will continue to do so. It can be compared to the telephone, television, and automobile as a paradigm shift in the way people work, communicate, and play. As the popular interface to what has been called the " information super-highway " , the Web purports to provide instantaneous access to vast quantities of information. However, the perception of time is always relative — access to information on today's Web is rarely instantaneous. While performance continues to improve over time from improvements in bandwidth and device latencies, users continue to desire yet additional speed [KPS + 99]. Likewise, content providers continue to make greater demands on bandwidth as it increases. Good interactive response-time has long been known to be essential for user satisfaction and productivity [DT82, Bra86, Roa98]. This is also true for the Web [RBP98, BBK00]. A widely-cited study from Zona Research [Zon99] provides evidence for the " eight second rule " — in electronic commerce, if a Web site takes more than eight seconds to load, the user is much more likely to become frustrated and leave the site. Thus there is also significant economic incentive for many content providers to provide a responsive Web experience. 1.1.1 Methods to improve Web response times Many factors contribute to a less-than-speedy Web experience, including heterogeneous network connectivity, real-world distances, and congestion in networks or servers due 1 to unexpected demand. As a result, many researchers (often as entrepreneurs) have considered the problem of improving Web response times. Some want to improve performance by achieving bandwidth increases and response time improvement through bigger " pipes " or alternative communication technologies. Others want to use the existing infrastructure more efficiently. Web caching, along with other forms of data dissemination, has been proposed as a technology that helps reduce network usage and server loads and improve typical response times experienced by the user. When successful , pre-loading Web objects into local caches can be used to further reduce web response times [PM96], and even to shift network loads from peak to non-peak periods [MRGM99]. Our interest is in pre-loading interactively, so that by dynamically pre-loading Web objects likely to be of interest into local caches, we may invisibly improve the user experience by improving the response time. To be more precise, let us start with a … 0.2% topic match

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0.0

2015

[368] ASSOCIATION FOR CONSUMER RESEARCH Satish S. Maheswarappa Journal Not Provided 2015 - 0 citations - Show abstract - Cite Abstract: of online information search on returns to search. We provide some answers by investigating effect of to search in ordered and unordered environments. An online simulated experiment was conducted and results were discussed. EXTENDED ABSTRACT Introduction and Motivation for the study Pre-purchase information search behaviour has been researched for long (for e.g., Copeland 1917) and with the evolution of the Internet it continues to attract considerable interest till date (e.g., Nordgren and as the incremental quality or lower price obtained by a consumer as a result of his/her search. Internet is a “revolution” that “has altered everything” (Hoffman 2000) resulting in increased information availability. This has led to the development of recommendation agents (RAs) to facilitate information processing and choice. Past works on the effectiveness of RAs is focused on 2008), order in which the product is listed (Xu and Kim 2008), and presentation mode or format (Hong et al. 2004b). More importantly, extant literature on decision making is inconclusive. Initial studies (for e.g., Diehl et al. 2003) showed that decision aids enabled consumers to make better decisions while expending substantially less effort. However, later studies (for e.g., Diehl 2005) showed that under certain circumstances increased search results in lower quality of consideration sets and ultimately lower choice. We intend to provide some explanation individual variables on returns to online search in ordered and unordered environments. It is known Internet differ by consumer type (Zettelmeyer et al. 2006). Moreover, consumer behavior is typically the result of interplay between personality and situational variables (Russell and Mehrabian 1976). Hence we study, How much should consumers search to optimize decision quality in ordered and unordered environments? How individual differences in extent of environments? Do decision satisfaction and decision Or do they increase with search time? Ordered environments are those where RAs evaluate all available options on behalf of the consumer and order options in decreasing expected utility. In unordered environments, options are listed in random order of utility. The conceptual framework for the hypotheses in the subsequent section is derived from essentially two broad theoretical perspectives: (1) theory of information processing and (2) elaboration likelihood model (ELM). According to Beatty and Smith (1987) and Schmidt and Sprang (1996), ability and motivation of the individual will affect extent of information search. Correspondingly we chose to involvement on returns to search. Consumers’ primary motive for prepurchase search is to enhance the quality of the purchase according to the information processing theory of consumer choice (Bettman 1979), consumers have limited cognitive processing ability. If information is provided beyond this point, the performance of the individual will rapidly decline (Chewning and Harrell 1990) because they might get confused and consider irrelevant information. Consumers who search the least will have too little information and those that search the most too much, and those that H1A: In unordered environments, compared with low and high level of information search, medium level of search should result in better decision quality Paradoxically, while high amounts of information H1B: In unordered environments, increase in the extent of information search leads to (i) higher decision satisfaction, and (ii) higher decision In ordered environments, RAs conserve deeper search as later alternatives are lower in quality (Diehl 2005). Hence, H1C: In ordered environments, low levels of information search should result in (i) better decision quality, (ii) lower decision satisfaction, and knowledge has an inverted U-shaped relationship because they exhibit broad perceptual category breadth. On the other hand, consumers with high breadth as a result they are more capable of elaborate information processing. But because of high selfsearch to a lesser extent (Hall et al. 2007). Consumers have narrower category breadth .This allows them to process greater amounts of, and more complex information. Hence, H2A: In unordered environments, lead to (i) better decision quality, and (ii) higher search time. H2B: In unordered environments, higher decision satisfaction, and (ii) higher decision H2C: In ordered environments, compared lower decision quality, and (ii) higher search time. H2D: In ordered environments, compared lower decision satisfaction, and (ii) lower decision Purchase decision involvement has been consumer brings to bear upon a purchase decision task (Mittal 1989). A person with a high need or interest will be more motivated to exert processing capacity. High involvement leads to greater searching (Brucks 1985). Hence we argue for a linear, as opposed to an inverted U-shaped relationship. H3A: In unordered environments, compared with low and medium levels of involvement, high levels of involvement lead to (i) better decision quality, and (ii) higher search time. H3B: In unordered environments, compared with low and medium levels of involvement, consumers with high levels of involvement will have (i) higher decision satisfaction, and (ii) higher H3C: In ordered environments, higher levels of involvement lead to (i) lower decision quality, (ii) higher decision satisfaction, (iii) higher 0.2% topic match

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11.6

2011

[369] Temporal Information Retrieval: Challenges and Opportunities Omar Alonso, ..., and Michael Gertz Journal Not Provided 2011 - 158 citations - Show abstract - Cite 0.2% topic match

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2.8

2012

[370] Scholarish: Google Scholar and its Value to the Sciences Jerry E. Gray, ..., and Fiona Taggart Issues in Science and Technology Librarianship 2012 - 33 citations - Show abstract - Cite 0.2% topic match

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2.7

2014

[371] Informational balance: slow principles in the theory and practice of information behaviour L. Poirier and L. Robinson J. Documentation 2014 - 27 citations - Show abstract - Cite 0.2% topic match

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4.4

2009

[372] Search Engine Use Behavior of Students and Faculty: User Perceptions and Implications for Future Research Oya Y. Rieger First Monday 2009 - 65 citations - Show abstract - Cite 0.2% topic match

0.2%

None

[373] From the Selectedworks of Andrea L Patalano Weighing Waiting: the Influence of Information Certainty and Delay Penalty on Waiting Behavior Weighing Waiting: the Influence of Information Certainty and Delay Penalty on Waiting for Noninstrumental Information Samuel M. Duncan, ..., and Alexandra Sedlovskaya Journal Not Provided None - 3 citations - Show abstract - Cite 0.2% topic match

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3.6

2007

[374] Time pressure in risky decision-making: effect on risk defusing O. Huber and U. Kunz Psychology Science 2007 - 61 citations - Show abstract - Cite 0.2% topic match

0.2%

0.5

2009

[375] The Effect of Counterproductive Time on Online Task Completion J. Nantel and S. Sénécal ACR Asia-Pacific Advances 2009 - 7 citations - Show abstract - Cite 0.2% topic match

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1.1

1968

[376] On the Psychological Importance of Time in a Time Sharing System1 2 J. R. Carbonell, ..., and R. Nickerson Human Factors: The Journal of Human Factors and Ergonomics Society 1968 - 64 citations - Show abstract - Cite 0.2% topic match

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0.4

2010

[377] On the Identification of the Costs of Simultaneous Search J. Moraga-González, ..., and Z. Sándor Microeconomics: Search; Learning; Information Costs & Specific Knowledge; Expectation & Speculation eJournal 2010 - 5 citations - Show abstract - Cite 0.2% topic match

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1.3

2014

[378] Slow Delphi: An investigation into information behaviour and the Slow Movement Elizabeth Poirier and L. Robinson Journal of Information Science 2014 - 14 citations - Show abstract - Cite 0.2% topic match

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0.0

2017

[379] The Tortoise Or The Hare: Undergraduates, Information Literacy, and the Slow Movement M. Frank, ..., and Kim Bailey Journal Not Provided 2017 - 0 citations - Show abstract - Cite Abstract: According to Aesop, “slow and steady wins the race.” However, with the advent of the internet, federated searching, electronic books and articles, and library marketing, the moral of Aesop’s famous fable no longer seems relevant to undergraduate student researchers. Several studies (Connaway, Dickey, & Radford, 2011; Poirier & Robinson, 2014; Savolainen, 2006) indicate that time is a major factor for many information seekers. Satisfaction seems to be embedded in “quick and easy” rather than “slow and sure”. Unfortunately, this is at odds with several dispositions and knowledge practices listed in the Association of College and Research Library’s information literacy framework. These guidelines support the emergence of increasingly sophisticated search practices and the development of perspective, both of which require a slower, more reflective approach. Conversely, undergraduate students’ temptation toward “quick and easy” research fosters a hollow experience, lacking both time and depth required for the potential enjoyment of an invested research process. Getting caught up in the search, losing all sense of time, and reaching the satisfaction that comes with discovery are among the affective benefits of methodical research, as is “being in the zone” and an academic equivalent to a “runner’s high”. Learning about subtopics along the way, locating quality resources, and developing deeper perspective on a topic are academic benefits also to be shared with undergraduate students who have yet to realize the great returns of time well invested in a slower and more mindful research process. The challenge lies in convincing inexperienced student researchers of this. This presentation will provide an overview of current research about information seeking behaviors and undergraduate students; it will discuss introducing Slow Movement techniques during information literacy sessions as a “best practice”; and it will discuss pedagogical reasons to orient students to “slow and steady wins the race”. 0.2% topic match

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None

[380] Opus: University of Bath Online Publication Store Choice Overload in Search Engine Use? Pawitra Chiravirakul and S. Payne Journal Not Provided None - 11 citations - Show abstract - Cite 0.2% topic match

0.2%

None

[381] Googling the news Opportunities and challenges in studying news events through Google Search No author found Journal Not Provided None - 0 citations - Show abstract - Cite 0.2% topic match

0.1%

0.3

2008

[382] ConnectionWatch: passive monitoring of round-trip times at end-hosts D. Joumblatt and R. Teixeira Conference on Emerging Network Experiment and Technology 2008 - 5 citations - Show abstract - Cite 0.1% topic match

0.1%

0.6

2014

[383] Structured Intelligent Search Engine for effective information retrieval using query clustering technique and Semantic Web Prakasha S, ..., and Zhang H 2014 International Conference on Contemporary Computing and Informatics (IC3I) 2014 - 6 citations - Show abstract - Cite 0.1% topic match

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0.2

2001

[384] Psychological Time: Interval Length Judgments and Subjective Passage of Time Judgments L. Bunker and A. Chaudhary Indian Journal of Health and Wellbeing 2001 - 4 citations - Show abstract - Cite 0.1% topic match

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0.7

2013

[385] Boxing clever: how searchers use and adapt to a one-box library search Dana Mckay and G. Buchanan Australasian Computer-Human Interaction Conference 2013 - 8 citations - Show abstract - Cite 0.1% topic match

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1.6

2015

[386] Examining Personalization in Academic Web Search Sara Salehi, ..., and H. Ashman Proceedings of the 26th ACM Conference on Hypertext & Social Media 2015 - 14 citations - Show abstract - Cite 0.1% topic match

0.1%

0.0

2014

[387] About time : Temporality in interaction Martin Krampell Journal Not Provided 2014 - 0 citations - Show abstract - Cite 0.1% topic match

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2.6

2006

[388] Google Scholar and 100 Percent Availability of Information J. Pomerantz Information Technology and Libraries 2006 - 48 citations - Show abstract - Cite 0.1% topic match

0.1%

1.8

2012

[389] Google Scholar Usage: An Academic Library's Experience Ya Wang and Pamela Howard Journal of Web Librarianship 2012 - 22 citations - Show abstract - Cite 0.1% topic match

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1.8

2001

[390] The effects of download delay on performance and end-user satisfaction in an Internet tutorial Erica S. Davis and D. Hantula Comput. Hum. Behav. 2001 - 42 citations - Show abstract - Cite 0.1% topic match

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0.0

2014

[391] Skillnader i attityd till personliga sökresultat Maja Gidlund and S. Norrby Journal Not Provided 2014 - 0 citations - Show abstract - Cite 0.1% topic match

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1.2

1997

[392] Adaptive Responses to Time Pressure: The Effects of Experience on Tax Information Search Behavior Brian C. Spilker and Douglas F. Prawitt Journal Not Provided 1997 - 34 citations - Show abstract - Cite 0.1% topic match

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0.2

2007

[393] The Effect of Small Changes in Web Page Navigation Links on the Performance of Users Who Revisit Sites P. Kortum and L. Scharff Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2007 - 3 citations - Show abstract - Cite 0.1% topic match

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1.2

2012

[394] Revisiting undergraduate practical pharmacology Syed Shariq Naeem, ..., and Anil Kumar Journal of Pharmacology & Pharmacotherapeutics 2012 - 15 citations - Show abstract - Cite 0.1% topic match

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3.3

2019

[395] A Comparative Study of Perceptions and Use of Google Scholar and Academic Library Discovery Systems K. Oh and Mónica Colón-Aguirre Coll. Res. Libr. 2019 - 16 citations - Show abstract - Cite 0.1% topic match

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1.7

2005

[396] Google Scholar™ and libraries: point/counterpoint M. Kesselman and S. Watstein Reference Services Review 2005 - 32 citations - Show abstract - Cite 0.1% topic match

0.1%

26.2

1996

[397] Using predictive prefetching to improve World Wide Web latency V. Padmanabhan and J. Mogul Comput. Commun. Rev. 1996 - 735 citations - Show abstract - Cite 0.1% topic match

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0.3

2004

[398] Understanding and reducing web delays K. Curran and Connor Duffy International Journal of Network Management 2004 - 7 citations - Show abstract - Cite 0.1% topic match

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0.0

2016

[399] Comparison of Map@500 Scores (cw09-a) for Rank-s and Taily Instances. a C R O N Y M S Csi Centralized Sample Index Wand Weighted and Xii Yubin Kim Journal Not Provided 2016 - 0 citations - Show abstract - Cite Abstract: Selective search is a modern distributed search architecture designed to reduce the computational cost of large-scale search. Selective search creates topical shards that are deliberately contentskewed, placing highly similar documents together in the same shard. During query time, rather than searching the entire corpus, a resource selection algorithm selects a subset of the topic shards likely to contain documents relevant to the query and search is only performed on these shards. This substantially reduces total computational costs of search while maintaining comparable accuracy to exhaustive distributed search. Prior work has proven selective search to be effective in smaller scale, single query-at-a-time environments. This thesis aims to bring selective search to wider adoption by addressing the questions related to efficiency and effectiveness in a practical implementation. The first set of investigations relate to the efficiency of selective search in a live system. A practical implementation of selective search would use a fully parallel query processing environment to maximize throughput but it is unknown whether selective search would retain is efficiency advantage over exhaustive search in this environment. This proposal investigated this research question using a detailed simulator of selective search. It presents load-balancing solutions to manage unequal popularity of shards and balance the cost of resource selection. Overall, it was found that with proper load management, the efficiency claims of prior work remain relevant in a fully parallel processing setting, and furthermore applicable to larger computational environments as well. Production systems also use query optimization techniques such as dynamic pruning. In this work, selective search was combined with WAND, a common dynamic pruning algorithm and it was shown that selective search and WAND had better-than-additive gains due to the long posting lists of the topically focused shards. The second half of the proposal research address concerns relating to the accuracy of selective search systems. Prior work has shown that a single instance of selective search was shown to have reasonable accuracy when averaged over multiple query sets. However, this selective search system contained non-deterministic steps and the variance of accuracy across different system instances was unclear. This thesis investigated the effects of random decisions on the accuracy of selective search and found the variance across system instances is acceptable. During the course of the thesis research, it was found that resource selection remains a major source of error in a selective search system and that there is room for improvement. Thus, a new resource selection algorithm was explored, using the pre-existing statistics collected by Block-Max WAND, a variant of the WAND optimization algorithm. We found that in a single-term query, the new method proved more effective than existing methods and comparable to exhaustive search. The dissertation research will continue improving the accuracy of selective search, including: developing more accurate resource selection; developing a stable selective search variant which closely reproduces the results of exhaustive search; and complex result merging to enable shardspecific search optimizations. Ultimately, the thesis aims to enable cost and energy-efficient use of large-scale data collections in not only information retrieval research, but also in other fields such as text mining and question answering, in academia and industry alike, fueling future innovation. 0.1% topic match

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0.1

2013

[400] Experiment on Action Characteristics of Web Information Retrieval Based on User Types and Browsing Time Jun'ichi Endou and Takehiko Murakawa Joho Chishiki Gakkaishi 2013 - 1 citations - Show abstract - Cite 0.1% topic match

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0.4

2005

[401] An Investigation into Factors Associated with Web Page Download Delay K. Curran and N. Broderick Int. J. Bus. Data Commun. Netw. 2005 - 7 citations - Show abstract - Cite 0.1% topic match

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0.5

2007

[402] A Search Engine Architecture Based on Collection Selection D. Puppin Journal Not Provided 2007 - 8 citations - Show abstract - Cite Abstract: In this thesis, we present a distributed architecture for a Web search engine, based on the concept of collection selection. We introduce a novel approach to partition the collection of documents, able to greatly improve the effectiveness of standard collection selection techniques (CORI), and a new selection function outperforming the state of the art. Our technique is based on the novel query-vector (QV) document model, built from the analysis of query logs, and on our strategy of co-clustering queries and documents at the same time. Incidentally, our partitioning strategy is able to identify documents that can be safely moved out of the main index (into a supplemental index), with a minimal loss in result accuracy. In our test, we could move 50% of the collection to the supplemental index with a minimal loss in recall. By suitably partitioning the documents in the collection, our system is able to select the subset of servers containing the most relevant documents for each query. Instead of broadcasting the query to every server in the computing platform, only the most relevant will be polled, this way reducing the average computing cost to solve a query. We introduce a novel strategy to use the instant load at each server to drive the query routing. Also, we describe a new approach to caching, able to incrementally improve the quality of the stored results. Our caching strategy is effectively both in reducing computing load and in improving result quality. By combining these innovations, we can achieve extremely high figures of precision, with a reduced load w.r.t. full query broadcasting. Our system can cover 65% results offered by a centralized reference index (competitive recall at 5), with a computing load of only 15.6%, i.e. a peak of 156 queries out of a shifting window of 1000 queries. This means about 1/4 of the peak load reached when broadcasting queries. The system, with a slightly higher load (24.6%), can cover 78% of the reference results. The proposed architecture, overall, presents a trade-off between computing cost and result quality, and we show how to guarantee very precise results in face of a dramatic reduction to computing load. This means that, with the same computing infrastructure, our system can serve more users, more queries and more documents. 0.1% topic match

0.1%

0.1

1978

[403] A Theory of Semi-Permanent Wage Search Stephen Mccafferty Southern Economic Journal 1978 - 3 citations - Show abstract - Cite 0.1% topic match

0.1%

3.3

2011

[404] Psychological Time: Interval Length Judgments and Subjective Passage of Time Judgments Madalina Sucala, ..., and Daniel David https://doi.org/10.4000/CPL.4998 2011 - 44 citations - Show abstract - Cite 0.1% topic match

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0.1

2009

[405] System response times in a simulated driving task : effects on performance, visual attention, subjective state and time estimation T. Bauer Journal Not Provided 2009 - 1 citations - Show abstract - Cite 0.1% topic match

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0.3

2006

[406] Semantics-based customization of information retrieval on the world wide web D. McLeod and Yun Chen Journal Not Provided 2006 - 6 citations - Show abstract - Cite 0.1% topic match

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0.5

2008

[407] Standing on the Shoulders of Libraries: A Holistic and Rhetorical Approach to Teaching Google Scholar C. Potter Journal of Library Administration 2008 - 8 citations - Show abstract - Cite 0.1% topic match

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0.1

2015

[408] Students' perceptions of personalized filters on their own information literacy skills G. Mcdermott Journal Not Provided 2015 - 1 citations - Show abstract - Cite 0.1% topic match

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1.2

2002

[409] Paging vs. Scrolling: Looking for the Best Way to Present Search Results B. Chaparro, ..., and Marissa Fernandez Journal Not Provided 2002 - 26 citations - Show abstract - Cite 0.0% topic match

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0.4

2015

[410] Web performance acceleration by caching rendering results Yuusuke Nakano, ..., and H. Miyahara 2015 17th Asia-Pacific Network Operations and Management Symposium (APNOMS) 2015 - 4 citations - Show abstract - Cite 0.0% topic match

0.0%

0.0

2011

[411] Web search behaviour of students H. Desai and R. Quilling Journal Not Provided 2011 - 0 citations - Show abstract - Cite 0.0% topic match

0.0%

3.9

2022

[412] Understanding the Impact of Time Pressure and Automation Support in a Visual Search Task Tobias Rieger and D. Manzey Human factors 2022 - 8 citations - Show abstract - Cite 0.0% topic match

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0.0

1997

[413] The Effect of Internet Delay on the Design of Distributed Multimedia Documents A. Sears and M. Borella Interacción 1997 - 0 citations - Show abstract - Cite 0.0% topic match

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1.9

1999

[414] Discrimination of changes in latency during head movement S. Ellis, ..., and S. Ehrlich Interacción 1999 - 47 citations - Show abstract - Cite 0.0% topic match

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1.4

2016

[415] When time meets information retrieval: Past proposals, current plans and future trends Bilel Moulahi, ..., and S. Yahia Journal of Information Science 2016 - 11 citations - Show abstract - Cite 0.0% topic match

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0.4

2003

[416] An experimental study of Web switching behavior James R. Otto, ..., and William P. Wagner Human systems management 2003 - 8 citations - Show abstract - Cite 0.0% topic match

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0.0

2013

[417] Effective Information Search and Retrieval for Answering Time Sensitive Queries Queries V Bharath Kumar Journal Not Provided 2013 - 0 citations - Show abstract - Cite 0.0% topic match

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3.0

2006

[418] Intermittent search strategies: When losing time becomes efficient O. Bénichou, ..., and R. Voituriez EPL (Europhysics Letters) 2006 - 55 citations - Show abstract - Cite 0.0% topic match

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2.4

2020

[419] Unravelling the relationship between response time and user experience in mobile applications Mengli Yu, ..., and Huiwen Wang Internet Res. 2020 - 10 citations - Show abstract - Cite 0.0% topic match

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0.1

2009

[420] Latency and User Performance in Virtual Environments and Augmented Reality S. Ellis 2009 13th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications 2009 - 1 citations - Show abstract - Cite Abstract: System rendering latency has been recognized by senior researchers, such as Professor Fredrick Brooks of UNC (Turing Award 1999), as a major factor limiting the realism and utility of head-referenced display systems. Latency has been shown to reduce the user’s sense of immersion within a virtual environment, to disturb user interaction with virtual objects, and to contribute to motion sickness during some simulation tasks. Latency, however, is not just an issue for external display systems since finite nerve conduction rates and variation in transduction times in the human body’s sensors also pose problems for latency management within the nervous system. Some of the phenomena arising from the brain’s handling of sensory asynchrony due to latency will be discussed as a prelude to consideration of the effects of latency in interactive displays. The causes and consequences of the erroneous movement that appears in displays due to latency will be illustrated with examples of the user performance impact provided by several experiments. These experiments will review the generality of user sensitivity to latency when users judge either object or environment stability. Hardware and signal processing countermeasures will also be discussed. In particular the tuning of a simple extrapolative predictive filter not using a dynamic movement model will be presented. Results show that it is possible to adjust this filter so that the appearance of some latencies may be hidden without the introduction of perceptual artifacts such as overshoot. Several examples of the effects of user performance will be illustrated by three-dimensional tracking and tracing tasks executed in virtual environments. These experiments demonstrate classic phenomena known from work on manual control and show the need for very responsive systems if they are intended to support precise manipulation. The practical benefits of removing interfering latencies from interactive systems will be emphasized with some classic final examples from surgical telerobotics and humancomputer interaction. 0.0% topic match

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4.3

2015

[421] Toward Predicting the Outcome of an A/B Experiment for Search Relevance Lihong Li, ..., and I. Zitouni Proceedings of the Eighth ACM International Conference on Web Search and Data Mining 2015 - 41 citations - Show abstract - Cite 0.0% topic match

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0.6

1998

[422] Term-ordered query evaluation versus document-ordered query evaluation for large document databases Marcin Kaszkiel and J. Zobel Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 1998 - 15 citations - Show abstract - Cite Abstract: There are two main families of technique for eficient processing of ranked queries on large text collections: document-ordered processing and term-ordered processing. In this note we compare these techniques experimentally. We show that they have similar costs for short queries, but that for long queries document-ordered processing is much more costly. Overall, we conclude that term-ordered processing, with the refinements of limited accumulators and hierarchical index structuring, is the more eficient mechanism. Techniques for evaluation of ranked queries on large text collections are well developed. In a typical ranked system each document in the collection is heuristically assigned a score representing its similarity to the query, and the documents with the highest scores are returned to the user. The most efficient of the current systems are based on inverted files; query evaluation involves fetching of inverted files, processing them to determine similarity values, then fetching of the top-scoring documents. Typically the number of documents fetched is small, whereas a high proportion of documents in the collection will have a non-zero similarity. These evaluation techniques are used in many applications, ranging from the short queries posed to Internet search engines, typically of two to five words, to extended queries posed by searching experts and long queries generated by techniques such as query expansion and relevance feedback. These techniques can provide better effectiveness than straightforward ranking, but involve many more query terms and are thus lead to increases in query evaluation costs. There are two principal techniques for evaluation of ranked queries: term-ordered (TO) processing and document-ordered (DO) processing. Both are based on inverted files [2, 71, a data structure containing, for each term, a sorted inverted list of the identifiers of the documents in which the term appears and the frequency of the term in each document. We compare TO and DO processing experimentally. In TO processing, the inverted list of each term is processed in full before the next is considered. For each document d in which each term appears, a partial similarity value is computed from the inverted list. Each partial similarity value is added to an accumulator corresponding to d. When processing of the inverted lists is complete, the acPermission to make digital/hard copy of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage, the copyright notice, the title of the publication and its date appear, and notice is given that copying is by permission of ACM, Inc. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or fee. SIGIR’98, Melbourne, Australia @ 1998 ACM l-58113-015-5 8/98 $5.00. cumulators are sequentially processed to normalise them with regard to document length and to identify the highest normalised scores. This style of processing is used, for example, in SMART [4] and MG (61. A variant on TO processing is to limit the number of accumulators, to say 2% of the total number of documents, and to structure the lists hierarchically [2]. In this TO’ or “skipping” style of processing, rare terms are considered first, and are free to add accumulators, up to the limit, as new document identifiers are observed. When the accumulator limit is reached no further accumulators can be added, and only a fraction of the information in the subsequent inverted lists is used; the hierarchically structuring allows this information to be skipped, significantly reducing CPU time (for list and accumulator processing) and memory requirements (for accumulators). Reducing the limit on the number of accumulators simultaneously reduces both memory requirements and processing time, but also reduces the ability of the mechanism to identify relevant documents, that is, reduces its effectiveness. Another variant of TO processing is to reorder lists by in-document frequency, so that larger partial similarities are to the front of each inverted list. We do not experiment with frequency-sorting here (as it is incompatible with the broader aims of our research, into passage ranking), but it allows significant gains over TO’ processing [3]. In DO processing, the inverted lists for all the query terms are processed simultaneously, in document order. At each stage the least document identifier d in any list is found, all information about d is consumed from the front of all lists in which d is referenced, a similarity value is computed for d, and processing proceeds to the next least document. Only a small number of intermediate resultsfinal similarity values-are required. Thus DO processing has the advantage of not requiring memory space for accumulators, but has several potential disadvantages. First, either enough buffer space must be allocated to hold all inverted lists simultaneously or query evaluation times will rise because several disk accesses are required to fetch each inverted list; in contrast, with TO processing it is feasible to fetch the whole of all but the longest lists, because lists are fetched in turn. Second, as query length increases the cost of identifying the list with the least document identifier will gradually dominate, as this cost is O(n log n) in the number of query terms, while all other costs are asymptotically constant or linear. Third, with DO processing it is not possible to use optimisations such as skipping. Turtle and Flood’s analysis of the performance of ranking algorithms in limited memory suggests that DO is more efficient than TO [5]. However, the model of processing used in this analysis is based on simplifying assumptions that are not valid; in particular, these assumptions imply that the processing costs are linear in the volume of inverted index information required (which is false for DO) 0.0% topic match

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2015

[423] New Perspectives on Personalised Search Results: Expertise and Institutionalisation T. Tran and H. Yerbury Australian Academic & Research Libraries 2015 - 5 citations - Show abstract - Cite 0.0% topic match

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1973

[424] The latency of the skin resistance response and reaction time. M. Cowles Psychophysiology 1973 - 10 citations - Show abstract - Cite 0.0% topic match

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1998

[425] Effects of Variation in System Responsiveness on User Performance in Virtual Environments B. Watson, ..., and Victoria A. Spaulding Johnson Human Factors: The Journal of Human Factors and Ergonomics Society 1998 - 82 citations - Show abstract - Cite 0.0% topic match

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2013

[426] Research and Implementation of Improved Real-Time Crawler Modeling Xianglin Zuo, ..., and W. Zuo Applied Mechanics and Materials 2013 - 0 citations - Show abstract - Cite 0.0% topic match

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1.4

2014

[427] Transfer Understanding from Head Queries to Tail Queries Yangqiu Song, ..., and Shusen Wang Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management 2014 - 14 citations - Show abstract - Cite 0.0% topic match

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2013

[428] How quick can you click? The role of response D. Campbell, ..., and S. B. Olsen Journal Not Provided 2013 - 5 citations - Show abstract - Cite 0.0% topic match

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2003

[429] System Delay and Navigational Structure for Web Applications and the Importance of Measuring Workload Jennifer A. Al-Safar and Ann C. Fulop Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2003 - 0 citations - Show abstract - Cite 0.0% topic match

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[430] Googling the news Opportunities and challenges in studying news events through Google Search No author found Journal Not Provided None - 0 citations - Show abstract - Cite 0.0% topic match

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2023

[431] Delphic Costs and Benefits in Web Search: A utilitarian and historical analysis A. Broder and Preston Mcafee ArXiv 2023 - 1 citations - Show abstract - Cite - PDF 0.0% topic match

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2005

[432] 網路搜尋引擎對取閱學術資訊之影響：以Google Scholar為例 陳凱誌 https://doi.org/10.6846/TKU.2005.00478 2005 - 0 citations - Show abstract - Cite 0.0% topic match

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2009

[433] Feature selection for ranking using boosted trees Feng Pan, ..., and Gianluca Donato Proceedings of the 18th ACM conference on Information and knowledge management 2009 - 65 citations - Show abstract - Cite 0.0% topic match

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2016

[434] Switching strategies to optimize search M. Shlesinger Journal of Statistical Mechanics: Theory and Experiment 2016 - 0 citations - Show abstract - Cite 0.0% topic match

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2016

[435] How to define the user's tolerance of response time in using mobile applications R. Zhou, ..., and L. Zhou 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) 2016 - 9 citations - Show abstract - Cite 0.0% topic match

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1997

[436] Query space reduction in information retrieval F. Kelledy Journal Not Provided 1997 - 5 citations - Show abstract - Cite 0.0% topic match

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2003

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2019

[438] Impact of response latency on sponsored search Xiao Bai and B. B. Cambazoglu Inf. Process. Manag. 2019 - 9 citations - Show abstract - Cite 0.0% topic match

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2017

[439] Search predicts and changes patience in intertemporal choice C. Reeck, ..., and Eric J. Johnson Proceedings of the National Academy of Sciences of the United States of America 2017 - 67 citations - Show abstract - Cite Abstract: Significance People often make decisions with consequences that unfold over time. When facing such intertemporal choices, people use different search strategies. We examine how these search strategies differ and how they relate to patience in intertemporal choice. We demonstrate that search varies substantially across individuals and identify two main search strategies—comparative or integrative search. Importantly, comparative search correlates with greater patience and higher susceptibility to contextual influences on choice. We manipulated search using an unobtrusive technique, revealing a causal relationship between strategy and choice. Comparative searchers make more patient choices and exhibit larger framing effects than integrative searchers. An understanding of how differences in psychological processes change discounting can inform the design of behavioral interventions to improve consumer welfare. Intertemporal choice impacts many important outcomes, such as decisions about health, education, wealth, and the environment. However, the psychological processes underlying decisions involving outcomes at different points in time remain unclear, limiting opportunities to intervene and improve people’s patience. This research examines information-search strategies used during intertemporal choice and their impact on decisions. In experiment 1, we demonstrate that search strategies vary substantially across individuals. We subsequently identify two distinct search strategies across individuals. Comparative searchers, who compare features across options, discount future options less and are more susceptible to acceleration versus delay framing than integrative searchers, who integrate the features of an option. Experiment 2 manipulates search using an unobtrusive method to establish a causal relationship between strategy and choice, randomly assigning participants to conditions promoting either comparative or integrative search. Again, comparative search promotes greater patience than integrative search. Additionally, when participants adopt a comparative search strategy, they also exhibit greater effects of acceleration versus delay framing. Although most participants reported that the manipulation did not change their behavior, promoting comparative search decreased discounting of future rewards substantially and speeded patient choices. These findings highlight the central role that heterogeneity in psychological processes plays in shaping intertemporal choice. Importantly, these results indicate that theories that ignore variability in search strategies may be inadvertently aggregating over different subpopulations that use very different processes. The findings also inform interventions in choice architecture to increase patience and improve consumer welfare. 0.0% topic match

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2001

[440] Adaptive multi-agent system for information retrieval S. Maleki-Dizaji, ..., and J. Siddiqqi https://doi.org/10.1117/12.446766 2001 - 0 citations - Show abstract - Cite 0.0% topic match

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2005

[441] Examining the effects of caching, compression and popup filtering on internet surfing K. Curran and Connor Duffy International Journal of Communication Systems 2005 - 0 citations - Show abstract - Cite 0.0% topic match

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2018

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2006

[443] Examining the claims of Google Scholar as a serious information source B. White Journal Not Provided 2006 - 30 citations - Show abstract - Cite 0.0% topic match

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2012

[444] A Time-Enhanced Topic Clustering Approach for News Web Search Jie Zhao, ..., and Peiquan Jin Journal Not Provided 2012 - 11 citations - Show abstract - Cite 0.0% topic match

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2009

[445] Chronism Theory, Culture, and System Delay: A Longitudinal Study of Post-Apartheid South Africa G. Rose, ..., and D. Straub J. Glob. Inf. Manag. 2009 - 14 citations - Show abstract - Cite 0.0% topic match

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2011

[446] A Student Perspective on e-Resource Discovery: Has the Google Factor Changed Publisher Platform Searching Forever? H. Timpson and Gemma Sansom The Serials Librarian 2011 - 16 citations - Show abstract - Cite 0.0% topic match

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2008

[447] Efficiency Issues in Information Retrieval Workshop Ivana Podnar Zarko-FER and Ranieri Baraglia-ISTI Journal Not Provided 2008 - 2 citations - Show abstract - Cite 0.0% topic match

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2007

[448] Social Search Engines M. Marchiori Int. J. Bifurc. Chaos 2007 - 3 citations - Show abstract - Cite 0.0% topic match

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2015

[449] Search Engine++: A Novel framework for Future Information Sharing Narayanan Srinivasn Journal Not Provided 2015 - 0 citations - Show abstract - Cite 0.0% topic match

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2021

[450] Diurnal Variation in Visual Simple Reaction Time between and within Genders in Young Adults: An Exploratory, Comparative, Pilot Study Hanumantha S, ..., and R. Shastry The Scientific World Journal 2021 - 5 citations - Show abstract - Cite 0.0% topic match

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2010

[451] Impact of information overload for visual search on Web pages: An eye-tracking study Wanxuan Lu, ..., and Ning Zhong IEEE/ICME International Conference on Complex Medical Engineering 2010 - 8 citations - Show abstract - Cite 0.0% topic match

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11.9

2014

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2016

[453] The Effects of Latency on 3D Interactive Data Visualizations A. Korenevsky https://doi.org/10.15368/theses.2016.37 2016 - 0 citations - Show abstract - Cite 0.0% topic match

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2012

[454] Delaying information search Y. Shani, ..., and M. Zeelenberg Judgment and Decision Making 2012 - 9 citations - Show abstract - Cite 0.0% topic match

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25.1

2015

[455] Factors influencing the latency of simple reaction time G. Rousselet, ..., and B. Reed Frontiers in Human Neuroscience 2015 - 234 citations - Show abstract - Cite 0.0% topic match

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2012

[456] State of the art process in query processing ranking system Bazeer Ahamed B and S. Hariharan 2012 Fourth International Conference on Advanced Computing (ICoAC) 2012 - 7 citations - Show abstract - Cite 0.0% topic match

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4.1

2019

[457] The effects of perceived chronic pressure and time constraint on information search behaviors and experience Chang Liu, ..., and Fan Yang Inf. Process. Manag. 2019 - 20 citations - Show abstract - Cite 0.0% topic match

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1993

[458] An Empirical Investigation of Returns to Search B. Ratchford and N. Srinivasan Marketing Science 1993 - 148 citations - Show abstract - Cite 0.0% topic match

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2009

[459] Query-Driven Indexing in Large-Scale Distributed Systems G. Skobeltsyn https://doi.org/10.5075/EPFL-THESIS-4280 2009 - 10 citations - Show abstract - Cite Abstract: Efficient and effective search in large-scale data repositories requires complex indexing solutions deployed on a large number of servers. Web search engines such as Google and Yahoo! already rely upon complex systems to be able to return relevant query results and keep processing times within the comfortable sub-second limit. Nevertheless, the exponential growth of the amount of content on the Web poses serious challenges with respect to scalability. Coping with these challenges requires novel indexing solutions that not only remain scalable but also preserve the search accuracy. In this thesis we introduce and explore the concept of query-driven indexing – an index construction strategy that uses caching techniques to adapt to the querying patterns expressed by users. We suggest to abandon the strict difference between indexing and caching, and to build a distributed indexing structure, or a distributed cache, such that it is optimized for the current query load. Our experimental and theoretical analysis shows that employing query-driven indexing is especially beneficial when the content is (geographically) distributed in a Peer-to-Peer network. In such a setting extensive bandwidth consumption has been identified as one of the major obstacles for efficient large-scale search. Our indexing mechanisms combat this problem by maintaining the query popularity statistics and by indexing (caching) intermediate query results that are requested frequently. We present several indexing strategies for processing multi-keyword and XPath queries over distributed collections of textual and XML documents respectively. Experimental evaluations show significant overall traffic reduction compared to the state-of-the-art approaches. We also study possible query-driven optimizations for Web search engine architectures. Contrary to the Peer-to-Peer setting, Web search engines use centralized caching of query results to reduce the processing load on the main index. We analyze real search engine query logs and show that the changes in query traffic that such a results cache induces fundamentally affect indexing performance. In particular, we study its impact on index pruning efficiency. We show that combination of both techniques enables efficient reduction of the query processing costs and thus is practical to use in Web search engines. 0.0% topic match

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2006

[460] Abdeckung und Aktualität des Suchdienstes Google Scholar Philipp Mayr and A. Walter Journal Not Provided 2006 - 11 citations - Show abstract - Cite 0.0% topic match

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