Discovering Rules for Clustering and Predicting Asynchronous Events

Tim Oates, David Jensen, and Paul R. Cohen

A wide variety of complex systems, ranging from nuclear power plants to governments, generate asynchronous events. We present Multi-Event Dependency Detection (MEDD), a novel algorithm for acquiring event correlation rules from historical logs of asynchronous events. Given a new stream of events being generated in real time, the rules support two important functions: clustering sets of related events and predicting events that will occur in the future.

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