Event Log Analysis
Event log analysis is known as event composition in active databases, chronicle recognition in artificial intelligence and as real-time logic evaluation in real-time systems. Essentially, event log analysis is used for pattern matching, filtering of event occurrences, and aggregation of event occurrences into composite event occurrences. Commonly, dynamic programming strategies from algorithms are employed to save results of previous analyses for future use, since, for example, the same pattern may be match with the same event occurrences in several consecutive analysis processing. In contrast to general rule processing (employed to assert new facts from other facts, cf. inference engine) that is usually based on backtracking techniques, event log analysis algorithms are commonly greedy; for example, when a composite is said to have occurred, this fact is never revoked as may be done in a backtracking based algorithm.
Several mechanisms have been proposed for event log analysis: finite state automata, Petri nets, procedural (either based on an imperative programming language or an object-oriented programming languages), a modification of Boyer–Moore string search algorithm, and simple temporal networks.
Read more about this topic: Event Monitoring
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