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Discovering pareto-optimal process models: a comparison of MOEA techniques

Published: 06 July 2018 Publication History

Abstract

Process mining aims at discovering the workflow of a process from the event logs that provide insights into organizational processes for improving these processes and their support systems. Ideally a process mining algorithm should produce a model that is simple, precise, general and fits the available logs. A conventional process mining algorithm typically generates a single process model that may not describe the recorded behavior effectively. Recently, Pareto multi-objective evolutionary algorithms have been used to generate several competing process models from the event logs. Subsequently, a user can choose a model based on his/her preference. In this paper, we have used three second-generation MOEA techniques, namely, PAES, SPEA-II, and NSGA-II, for generating a set of non-dominated process models. Using the BPI datasets, we demonstrate the efficacy of NSGA-II with respect to solution quality over its competitor algorithms.

References

[1]
Joos CAM Buijs, Boudewijn F van Dongen, and Wil MP van der Aalst. 2013. Discovering and navigating a collection of process models using multiple quality dimensions. In International Conference on Business Process Management. Springer, 3--14.
[2]
Joshua D Knowles and David W Corne. 2000. Approximating the nondominated front using the Pareto archived evolution strategy. Evolutionary computation 8, 2 (2000), 149--172.
[3]
Process Mining. 2011. Discovery, Conformance and Enhancement of Business Processes. Springer-Verlag 8 (2011), 18.
[4]
Nidamarthi Srinivas and Kalyanmoy Deb. 1994. Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation 2, 3 (1994), 221--248.
[5]
Wil MP van der Aalst. 2016. Process mining: data science in action. Springer.
[6]
Eckart Zitzler, Marco Laumanns, and Lothar Thiele. 2001. SPEA2: Improving the strength Pareto evolutionary algorithm. (2001).

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Published In

cover image ACM Conferences
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2018
1968 pages
ISBN:9781450357647
DOI:10.1145/3205651
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 July 2018

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Author Tags

  1. evolutionary algorithms
  2. multi-objective optimization
  3. pareto-front
  4. process discovery
  5. process model quality dimensions

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