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Augmenting Modelers with Semantic Autocompletion of Processes

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Business Process Management Forum (BPM 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 427))

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Abstract

Business process modelers need to have expertise and knowledge of the domain that may not always be available to them. Therefore, they may benefit from tools that mine collections of existing processes and recommend element(s) to be added to a new process in design time. In this paper, we present a method for process autocompletion at design time, that is based on the semantic similarity of sub-processes. By converting sub-processes to textual paragraphs and encoding them as numerical vectors, we can find semantically similar ones, and thereafter recommend the next element. To achieve this, we leverage a state-of-the-art technique for encoding natural language as vectors. We evaluate our approach on open source and proprietary datasets and show that our technique is accurate for processes in various domains.

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Notes

  1. 1.

    Computed as average over all four datasets, but not shown due to space limitation.

  2. 2.

    Not visualized due to space limitation.

  3. 3.

    Some precision and recall values are rounded to 0 when only two decimal places are used. For such cases, we use higher precision values to compute the ratio.

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Author information

Authors and Affiliations

  1. Nokia Bell Labs, Kfar Sava, Israel

    Maayan Goldstein

  2. Nokia Bell Labs, Antwerp, Belgium

    Cecilia González-Álvarez

Authors
  1. Maayan Goldstein
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  2. Cecilia González-Álvarez
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Corresponding author

Correspondence to Maayan Goldstein .

Editor information

Editors and Affiliations

  1. The University of Melbourne, Carlton, VIC, Australia

    Artem Polyvyanyy

  2. NICTA Queensland Res Lab, Queensland University of Technology, Brisbane, QLD, Australia

    Moe Thandar Wynn

  3. EB24, Ghent University, Ghent, Belgium

    Amy Van Looy

  4. University of Ulm, Ulm, Germany

    Manfred Reichert

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Goldstein, M., González-Álvarez, C. (2021). Augmenting Modelers with Semantic Autocompletion of Processes. In: Polyvyanyy, A., Wynn, M.T., Van Looy, A., Reichert, M. (eds) Business Process Management Forum. BPM 2021. Lecture Notes in Business Information Processing, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-030-85440-9_2

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  • DOI: https://doi.org/10.1007/978-3-030-85440-9_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85439-3

  • Online ISBN: 978-3-030-85440-9

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