Genetic Process Mining

The topic of process mining has attracted the attention of both researchers and tool vendors in the Business Process Management (BPM) space. The goal of process mining is to discover process models from event logs, i.e., events logged by some information system are used to extract information about activities and their causal relations. Several algorithms have been proposed for process mining. Many of these algorithms cannot deal with concurrency. Other typical problems are the presence of duplicate activities, hidden activities, non-free-choice constructs, etc. In addition, real-life logs contain noise (e.g., exceptions or incorrectly logged events) and are typically incomplete (i.e., the event logs contain only a fragment of all possible behaviors). To tackle these problems we propose a completely new approach based on genetic algorithms. As can be expected, a genetic approach is able to deal with noise and incompleteness. However, it is not easy to represent processes properly in a ...

• Type: Conference
• Year: 2005
• Where: APN
• Authors: Wil M. P. van der Aalst, Ana Karla A. de Medeiros, A. J. M. M. Weijters
• Keywords: APN 2005, Artificial Intelligence, Event Logs, Genetic Algorithms, Process Mining