Discovering Rules for Clustering and Predicting Asynchronous Events
T. Oates, d. Jensen and P. R. Cohen. 1998. Discovering rules for clustering and predicting asynchronous events. Predicting the Future: AI Approaches to Time-Series Problems: Papers from the 1998 Workshop. 73-79.
- Abstract
A wide variety of complex systems generate asynchronous events, including nuclear power plants, computer networks, governments, relational database systems and operating systems. 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
generated in real time, the rules enable two important activities: clustering sets of related events and
predicting events that will occur in the future. The
former activity supports data reduction so that human monitors can more easily understand the state of
the system generating the events, and the latter activity facilitates prediction of future states of the system
by reasoning about events that are likely to occur.
Medd's utility is evaluated in experiments with event
logs generated by a simulated computer network and
encodings of Reuters news stories describing events in
the Persian Gulf during 1996 and 1997.- Text
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