國立虎尾科技大學 |

Understanding Data Analysis Activity via Log Analysis.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Understanding Data Analysis Activity via Log Analysis./
Author:	Alspaugh, Sara M.
Description:	1 online resource (211 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.
Contained By:	Dissertation Abstracts International79-05B(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9780355576849

Understanding Data Analysis Activity via Log Analysis.
Alspaugh, Sara M.

Understanding Data Analysis Activity via Log Analysis. - 1 online resource (211 pages)

Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

The study of user analysis behavior is of interest to the designers of analysis tools. Specific questions studied include: What types of tasks do users perform using this analysis tool? What approaches do users take to gain insights? What interface features help or hinder users in their work? What are the the distinguishing characteristics of different types of users? These questions are often investigated through controlled experiments, observational studies, user interviews, or surveys. An alternative avenue of investigation is to analyze the logs---the records of user activity---generated by analysis tools themselves. In this dissertation we present two case studies using log analysis to understand user behavior. In the first, we analyze records of user queries from Splunk, a system for log analysis, as well as a survey of Splunk users. In the second, we analyze detailed event logs and application state from Tableau, a system for visualizing relational data. We focus in particular on methods of identifying higher-level units of activity, which we refer to as tasks. We include a discussion of the particular challenges associated with collecting and analyzing log data from analysis systems. In addition to this discussion, our contributions include the description of two different approaches for identifying higher-level analysis activity from logs and a summary of the tasks represented in our datasets.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355576849Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Understanding Data Analysis Activity via Log Analysis.
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100 1 $a Alspaugh, Sara M. $3 1182279
245 1 0 $a Understanding Data Analysis Activity via Log Analysis.
264 0 $c 2017
300 $a 1 online resource (211 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-05(E), Section: B.
500 $a Advisers: Randy Katz; Marti Hearst.
502 $a Thesis (Ph.D.) $c University of California, Berkeley $d 2017.
504 $a Includes bibliographical references
520 $a The study of user analysis behavior is of interest to the designers of analysis tools. Specific questions studied include: What types of tasks do users perform using this analysis tool? What approaches do users take to gain insights? What interface features help or hinder users in their work? What are the the distinguishing characteristics of different types of users? These questions are often investigated through controlled experiments, observational studies, user interviews, or surveys. An alternative avenue of investigation is to analyze the logs---the records of user activity---generated by analysis tools themselves. In this dissertation we present two case studies using log analysis to understand user behavior. In the first, we analyze records of user queries from Splunk, a system for log analysis, as well as a survey of Splunk users. In the second, we analyze detailed event logs and application state from Tableau, a system for visualizing relational data. We focus in particular on methods of identifying higher-level units of activity, which we refer to as tasks. We include a discussion of the particular challenges associated with collecting and analyzing log data from analysis systems. In addition to this discussion, our contributions include the description of two different approaches for identifying higher-level analysis activity from logs and a summary of the tasks represented in our datasets.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Information science. $3 561178
655 7 $a Electronic books. $2 local $3 554714
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773 0 $t Dissertation Abstracts International $g 79-05B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10616939 $z click for full text (PQDT)