MINING EVENT DATA FOR ACTIONABLE PATTERNS

Abstract

A central problem in event management is constructing correlation rules. Doing so requires characterizing patterns of events for which actions should be taken (e.g., sequences of printer status changes that foretell a printer-off line event). In most cases, rule construction requires experts to identify problem patterns, a process that is time-consuming and error prone. Herein, we describe how data mining can be used to identify actionable patterns. In particular, we present efficient mining algorithms for three kinds of patterns found in event data: event bursts, periodicities, and mutually dependent events.