Readings in Database Systems: Fourth Edition
Readings in Database Systems: Fourth Edition
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
LearnPADS: automatic tool generation from ad hoc data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Web analytics and the art of data summarization
SLAML '11 Managing Large-scale Systems via the Analysis of System Logs and the Application of Machine Learning Techniques
Bridging the divide between software developers and operators using logs
Proceedings of the 34th International Conference on Software Engineering
Experiences with workload management in splunk
Proceedings of the 2012 workshop on Management of big data systems
Building blocks for exploratory data analysis tools
Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics
DevOps patterns to scale web applications using cloud services
Proceedings of the 2013 companion publication for conference on Systems, programming, & applications: software for humanity
| Hi-index | 0.00 |
Most modern systems generate abundant and diverse log data. With dwindling storage costs, there are fewer reasons to summarize or discard data. However, the lack of tools to efficiently store and cross-correlate heterogeneous datasets makes it tedious to mine the data for analytic insights. In this paper, we present Splunk, a semi-structured time series database that can be used to index, search and analyze massive heterogeneous datasets. We share observations, lessons and case studies from real world datasets, and demonstrate Splunk's power and flexibility for enabling insightful data mining searches.