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

doi:10.1109/CEC.2018.8477795

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 proceeding › Conference contribution › Academic › peer-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 language	English
Title of host publication	2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509060177
DOIs	https://doi.org/10.1109/CEC.2018.8477795
Publication status	Published - 2018
Event	2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018

Conference

Conference	2018 IEEE Congress on Evolutionary Computation, CEC 2018
Country/Territory	Brazil
City	Rio de Janeiro
Period	8/07/18 → 13/07/18

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10.1109/CEC.2018.8477795

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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

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

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.",

author = "{Da Silva}, {Gabriel L.C.} and Marcelo Fantinato and Peres, {Sarajane M.} and Reijers, {Hajo A.}",

year = "2018",

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language = "English",

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note = "2018 IEEE Congress on Evolutionary Computation, CEC 2018 ; Conference date: 08-07-2018 Through 13-07-2018",

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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 proceeding › Conference contribution › Academic › peer-review

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AB - 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.

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

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