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. (a) The four quality dimensions used to calculate process model fitness (from [1]). Stop? Select Change Evaluate Create [ Wednesday 30 th April, 2014 at 17:56-?? pages-version 0.1 ] (b) The different phases of a genetic process discovery algorithm, as given in [3].
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.