On extracting session data from activity logs

Mehrzadi, David; Feitelson, Dror G.

doi:10.1145/2367589.2367592

Cited by 17 publications

(16 citation statements)

References 23 publications

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“…Calculating the exact dwell time is not obvious, since the time spent on the last page of a session is generally not known. Adding to this limitation, there is yet no consensus on how to identify when a session actually ends [15]. Multitasking makes it even more problematic to accurately calculate dwell time 2 because of backpaging.…”

Section: Backpaging and Dwell Timementioning

confidence: 99%

Online multitasking and user engagement

Lehmann

Lalmas

Dupret

et al. 2013

Proceedings of the 22nd ACM International Conference on Information &Amp; Knowledge Management

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Users often access and re-access more than one site during an online session, effectively engaging in multitasking. In this paper, we study the effect of online multitasking on two widely used engagement metrics designed to capture users browsing behavior with a site. Our study is based on browsing data of 2.5M users across 760 sites encompassing diverse types of services such as social media, news and mail. To account for multitasking we need to redefine how user sessions are represented and we need to adapt the metrics under study. We introduce a new representation of user sessions: tree-streams -as opposed to the commonly used click-streams -present a more accurate picture of the browsing behavior of a user that includes how users switch between sites (e.g., hyperlinking, teleporting, backpaging). We then discuss a number of insights on multitasking patterns, and show how these help to better understand how users engage with sites. Finally, we define metrics that characterize multitasking during online sessions and show how they provide additional insights to standard engagement metrics.

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Section: Backpaging and Dwell Timementioning

confidence: 99%

Online multitasking and user engagement

Lehmann

Lalmas

Dupret

et al. 2013

Proceedings of the 22nd ACM International Conference on Information &Amp; Knowledge Management

View full text Add to dashboard Cite

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“…This technique also has the advantage of tailoring the threshold for each user separately, rather than assuming that a single threshold is suitable for all users [482]. For intuition, consider a graph of jobs as a function of time.…”

Section: End Boxmentioning

confidence: 99%

Workload Modeling for Computer Systems Performance Evaluation

Feitelson

2015

Self Cite

197

131

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Reliable performance evaluations require the use of representative workloads. This is no easy task since modern computer systems and their workloads are complex, with many interrelated attributes and complicated structures. Experts often use sophisticated mathematics to analyze and describe workload models, making these models difficult for practitioners to grasp. This book aims to close this gap by emphasizing the intuition and the reasoning behind the definitions and derivations related to the workload models. It provides numerous examples from real production systems, with hundreds of graphs. Using this book, readers will be able to analyze collected workload data and clean it if necessary, derive statistical models that include skewed marginal distributions and correlations, and consider the need for generative models and feedback from the system. The descriptive statistics techniques covered are also useful for other domains.

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“…According to the work of Mehrzadi, using the Arrival approach may lead to artifacts in the distribution of session durations [6]. Specifically, he shows that in web search data the distribution of session lengths exhibits a pronounced drop exactly at the threshold value that was used to define the sessions.…”

Section: Artifacts In the Distribution Of Session Lengths With Arrivalmentioning

confidence: 99%

On Identifying User Session Boundaries in Parallel Workload Logs

Zakay

Feitelson

2013

Job Scheduling Strategies for Parallel Processing

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Abstract. The stream of jobs submitted to a parallel supercomputer is actually the interleaving of many streams from different users, each of which is composed of sessions. Identifying and characterizing the sessions is important in the context of workload modeling, especially if a userbased workload model is considered. Traditionally, sessions have been delimited by long think times, that is, by intervals of more than, say, 20 minutes from the termination of one job to the submittal of the next job.We show that such a definition is problematic in this context, because jobs may be extremely long. As a result of including each job's execution in the session, we may get unrealistically long sessions, and indeed, users most probably do not always stay connected and wait for the termination of long jobs. We therefore suggest that sessions be identified based on proven user activity, namely the submittal of new jobs, regardless of how long they run.

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On extracting session data from activity logs

Cited by 17 publications

References 23 publications

Online multitasking and user engagement

Online multitasking and user engagement

Workload Modeling for Computer Systems Performance Evaluation

On Identifying User Session Boundaries in Parallel Workload Logs

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