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.
['Sara Alspaugh', 'Bei Di Chen', 'Jessica Lin', 'Archana Ganapathi', 'Marti A. Hearst', 'Randy H. Katz']
Analyzing log analysis: an empirical study of user log mining