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Analyzing Log Analysis: An Empirical Study of User Log Mining

Refereed Paper
Wednesday, November 12, 2014 - 2:45pm-3:00pm
Authors: 

S. Alspaugh, University of California, Berkeley and Splunk Inc.; Beidi Chen and Jessica Lin, University of California, Berkeley; Archana Ganapathi, Splunk Inc.; Marti A. Hearst and Randy Katz, University of California, Berkeley

Awarded Best Student Paper! 

Abstract: 

We present an in-depth study of over 200K log analysis queries from Splunk, a platform for data analytics. Using these queries, we quantitatively describe log analysis behavior to inform the design of analysis tools. This study includes state machine based descriptions of typical log analysis pipelines, cluster analysis of the most common transformation types, and survey data about Splunk user roles, use cases, and skill sets. We find that log analysis primarily involves filtering, reformatting, and summarizing data and that non-technical users increasingly need data from logs to drive their decision making. We conclude with a number of suggestions for future research.

S. Alspaugh, University of California, Berkeley, and Splunk Inc.

Beidi Chen, University of California, Berkeley

Jessica Lin, University of California, Berkeley

Archana Ganapathi, Splunk Inc.

Marti Hearst, University of California, Berkeley

Randy Katz, University of California, Berkeley

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