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GA-ProM: A Genetic Algorithm for Discovery of Complete Process Models from Unbalanced Logs

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Big-Data-Analytics in Astronomy, Science, and Engineering (BDA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13167))

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Abstract

Process mining is the practise of distilling a structured process description from a series of real executions. In past decades, different process discovery algorithms have been used to generate process models. In this paper, we propose a genetic mining algorithm (GA-ProM) for process discovery and compare it with other state-of-the-art algorithms, namely, \(\alpha \) \(^{++}\), genetic process mining, heuristic miner, and inductive logic programming. To evaluate the effectiveness of the proposed algorithm the experimentation was done on 21 synthetic event logs. The results show that the proposed algorithm outperforms the compared algorithms in generating the quality model.

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Authors and Affiliations

  1. Department of Computer Science, University of Delhi, Delhi, 110007, India

    Sonia Deshmukh & Naveen Kumar

  2. Shaheed Sukhdev College of Business Studies, University of Delhi, Delhi, 110007, India

    Shikha Gupta

Authors
  1. Sonia Deshmukh
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  2. Shikha Gupta
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  3. Naveen Kumar
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Correspondence to Shikha Gupta .

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Editors and Affiliations

  1. National Institute of Technology, Delhi, India

    Shelly Sachdeva

  2. University of Aizu, Aizu-wakamatsu city, Fukushima, Japan

    Yutaka Watanobe

  3. University of Aizu, Aizu-wakamatsu city, Fukushima, Japan

    Subhash Bhalla

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Deshmukh, S., Gupta, S., Kumar, N. (2022). GA-ProM: A Genetic Algorithm for Discovery of Complete Process Models from Unbalanced Logs. In: Sachdeva, S., Watanobe, Y., Bhalla, S. (eds) Big-Data-Analytics in Astronomy, Science, and Engineering. BDA 2021. Lecture Notes in Computer Science(), vol 13167. Springer, Cham. https://doi.org/10.1007/978-3-030-96600-3_15

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  • DOI: https://doi.org/10.1007/978-3-030-96600-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96599-0

  • Online ISBN: 978-3-030-96600-3

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