Discovery of Unstructured Business Processes Through Genetic Algorithms Using Activity Transitions-Based Completeness and Precision

Gabriel L.C. Da Silva, Marcelo Fantinato, Sarajane M. Peres, Hajo A. Reijers

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Process model discovery can be approached as an optimization problem, for which genetic algorithms have been used previously. However, the fitness functions used, which consider full log traces, have not been found adequate to discover unstructured processes. We propose a solution based on a local analysis of activity transitions, which proves effective for unstructured processes, most common in organizations. Our solution considers completeness and accuracy calculation for the fitness function.

Original languageEnglish
Title of host publication2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509060177
DOIs
Publication statusPublished - 2018
Event2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Conference

Conference2018 IEEE Congress on Evolutionary Computation, CEC 2018
CountryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

Fingerprint

Fitness Function
Business Process
Completeness
Genetic algorithms
Genetic Algorithm
Process Model
Industry
Trace
Optimization Problem

Cite this

Da Silva, G. L. C., Fantinato, M., Peres, S. M., & Reijers, H. A. (2018). Discovery of Unstructured Business Processes Through Genetic Algorithms Using Activity Transitions-Based Completeness and Precision. In 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings [8477795] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2018.8477795
Da Silva, Gabriel L.C. ; Fantinato, Marcelo ; Peres, Sarajane M. ; Reijers, Hajo A. / Discovery of Unstructured Business Processes Through Genetic Algorithms Using Activity Transitions-Based Completeness and Precision. 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018.
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Da Silva, GLC, Fantinato, M, Peres, SM & Reijers, HA 2018, Discovery of Unstructured Business Processes Through Genetic Algorithms Using Activity Transitions-Based Completeness and Precision. in 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings., 8477795, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE Congress on Evolutionary Computation, CEC 2018, Rio de Janeiro, Brazil, 8/07/18. https://doi.org/10.1109/CEC.2018.8477795

Discovery of Unstructured Business Processes Through Genetic Algorithms Using Activity Transitions-Based Completeness and Precision. / Da Silva, Gabriel L.C.; Fantinato, Marcelo; Peres, Sarajane M.; Reijers, Hajo A.

2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8477795.

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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Da Silva GLC, Fantinato M, Peres SM, Reijers HA. Discovery of Unstructured Business Processes Through Genetic Algorithms Using Activity Transitions-Based Completeness and Precision. In 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8477795 https://doi.org/10.1109/CEC.2018.8477795