Name
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. |
Year | Venue | Keywords |
|---|---|---|
2014 | LISA | user modeling,user surveys,log analysis,query logs,splunk |
Field | DocType | Citations |
Survey data collection,Data mining,Use case,Data analysis,Computer science,Filter (signal processing),Finite-state machine,Web log analysis software,User modeling,Empirical research | Conference | 7 |
PageRank | References | Authors |
0.64 | 32 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sara Alspaugh | 1 | 553 | 22.91 |
| Bei Di Chen | 2 | 16 | 6.53 |
| Jessica Lin 0003 | 3 | 7 | 0.64 |
| Archana Ganapathi | 4 | 860 | 54.96 |
| Marti A. Hearst | 5 | 7014 | 769.93 |
| Randy H. Katz | 6 | 16819 | 3018.89 |