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Mining Patterns Using Business Process Management
Abstract
Process mining provides an important bridge between data mining and business process analysis. This technique allows for the extraction of information from event logs. In general, there are two steps in process mining: correlation definition or discovery and then process inference or composition. Firstly, the authors mine small patterns from log traces of two applications; those patterns are the representation of the execution traces of a business process. In this step, the authors use existing techniques. The patterns are represented by finite state automaton or their regular expression. The final model is the combination of only two types of small patterns that are represented by the regular expressions (ab)* and (ab*c)*. Secondly, the authors compute these patterns in parallel and then combine those small patterns using the composition rules. They have two parties. The first is the mine, where the authors discover patterns from execution traces, and the second is the combination of these small patterns. The pattern mining and the composition is illustrated by the automaton existing techniques.
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