Genetic Process Mining: Alignment-based Process Model Mutation

Conference Contribution

Eck, van, M.L., Buijs, J.C.A.M. & Dongen, van, B.F. (2015). Genetic Process Mining: Alignment-based Process Model Mutation. In J. Mendling & F. Fournier (Eds.), Business Process Management Workshops (BPM 2014 International Workshops, Eindhoven, The Netherlands, September 7-8, 2014, Revised Papers) (pp. 291-303). (Lecture Notes in Business Information Processing, No. 202). Berlin: Springer. In Scopus Cited 4 times.

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Abstract

 

The Evolutionary Tree Miner (ETM) is a genetic process discovery algorithm that enables the user to guide the discovery process based on preferences with respect to four process model quality dimensions: replay fitness, precision, generalization and simplicity.

Traditionally, the ETM algorithm uses random creation of process models for the initial population, as well as random mutation and crossover techniques for the evolution of generations. In this paper, we present an approach that improves the performance of the ETM algorithm by enabling it to make guided changes to process models, in order to obtain higher quality models in fewer generations. The two parts of this approach are: (1) creating an initial population of process models with a reasonable quality; (2) using information from the alignment between an event log and a process model to identify quality issues in a given part of a model, and resolving those issues using guided mutation operations.