Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Rule Discovery in Telecommunication AlarmData
Journal of Network and Systems Management
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Mining intrusion detection alarms for actionable knowledge
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Motifs in Massive Time Series Databases
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Data-driven validation, completion and construction of event relationship networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Efficient pattern matching over event streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
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In large scale systems IT systems, for the purpose of management, we have what are known as "Networked Operations Centers" or NOCs. These NOCs are manned by support staff, known as operators. Data in the form of events from various sub-systems in the larger system is sent to the central event console so that the NOC operators can work on problems in the infrastructure before they have a significant impact on the availability and performance of business applications. The challenge for these NOC operators is that of managing the large number of incoming events. In this work, we address the problem of scale by proposing a data mining technique for automatic event correlation for large scale operations management systems.