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Enhancing Completion Time Prediction Through Attribute Selection

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Information Technology for Management: Emerging Research and Applications (AITM 2018, ISM 2018)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 346))

Abstract

Approaches have been proposed in process mining to predict the completion time of process instances. However, the accuracy levels of the prediction models depend on how useful the log attributes used to build such models are. A canonical subset of attributes can also offer a better understanding of the underlying process. We describe the application of two automatic attribute selection methods to build prediction models for completion time. The filter was used with ranking whereas the wrapper was used with hill-climbing and best-first techniques. Annotated transition systems were used as the prediction model. Compared to decision-making by human experts, only the automatic attribute selectors using wrappers performed better. The filter-based attribute selector presented the lowest performance on generalization capacity. The semantic reasonability of the selected attributes in each case was analyzed in a real-world incident management process.

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Notes

  1. 1.

    This paper details the approach and results published in a summarized preliminary version [13].

  2. 2.

    In the forward selection, the search initial point is a singleton attribute subset to which one new attribute is incorporated at each new step in the search.

  3. 3.

    Available at http://each.uspnet.usp.br/sarajane/?page_id=12.

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Acknowledgments

This work was funded by the São Paulo Research Foundation (Fapesp), Brazil; grants 2017/26491-1 and 2017/26487-4.

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Authors and Affiliations

  1. School of Arts, Sciences and Humanities, University of São Paulo, 1000 Arlindo Béttio St., Ermelino Matarazzo, São Paulo, SP, 03828-000, Brazil

    Claudio A. L. Amaral, Marcelo Fantinato & Sarajane M. Peres

  2. Department of Information and Computing Sciences, Utrecht University, Princetonplein 5, 3584 CC, Utrecht, The Netherlands

    Hajo A. Reijers

Authors
  1. Claudio A. L. Amaral
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  2. Marcelo Fantinato
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  3. Hajo A. Reijers
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  4. Sarajane M. Peres
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Corresponding author

Correspondence to Marcelo Fantinato .

Editor information

Editors and Affiliations

  1. University of Economics in Katowice, Katowice, Poland

    Ewa Ziemba

Appendix

Appendix

A brief description of the 15 attributes listed in Table 4 is presented in Table 10.

Table 10. Description of the 15 attributes used in the experiment
Full size table

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Amaral, C.A.L., Fantinato, M., Reijers, H.A., Peres, S.M. (2019). Enhancing Completion Time Prediction Through Attribute Selection. In: Ziemba, E. (eds) Information Technology for Management: Emerging Research and Applications. AITM ISM 2018 2018. Lecture Notes in Business Information Processing, vol 346. Springer, Cham. https://doi.org/10.1007/978-3-030-15154-6_1

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  • DOI: https://doi.org/10.1007/978-3-030-15154-6_1

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  • Print ISBN: 978-3-030-15153-9

  • Online ISBN: 978-3-030-15154-6

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