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Towards Measuring Process Model Granularity via Natural Language Analysis

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Business Process Management Workshops (BPM 2013)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 171))

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Abstract

Nowadays business process modeling is an integral part of many organizations to document and redesign complex organizational processes. Particularly due to the large number of process models, quality assurance represents an important issue in many organizations. While many quality aspects are well understood and can be automatically checked with existing tools, there is currently no possibility to support modelers in maintaining a consistent degree of granularity. In this paper, we leverage natural language analysis in process models to introduce a novel set of metrics that indicate the granularity of process models. We evaluate the proposed metrics using two hierarchically organized process model collections from practice. Statistical tests demonstrate the expressive power of the proposed metrics.

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Notes

  1. 1.

    see Oxford Online Dictionary for granularity and granular.

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Author information

Authors and Affiliations

  1. Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany

    Henrik Leopold & Fabian Pittke

  2. WU Vienna, Welthandelsplatz 1, 1020, Vienna, Austria

    Jan Mendling

Authors
  1. Henrik Leopold
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  2. Fabian Pittke
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  3. Jan Mendling
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Corresponding author

Correspondence to Henrik Leopold .

Editor information

Editors and Affiliations

  1. University of Rostock, Rostock, Germany

    Niels Lohmann

  2. Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of (South Korea)

    Minseok Song

  3. Stockholm University, Stockholm, Sweden

    Petia Wohed

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Leopold, H., Pittke, F., Mendling, J. (2014). Towards Measuring Process Model Granularity via Natural Language Analysis. In: Lohmann, N., Song, M., Wohed, P. (eds) Business Process Management Workshops. BPM 2013. Lecture Notes in Business Information Processing, vol 171. Springer, Cham. https://doi.org/10.1007/978-3-319-06257-0_33

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  • DOI: https://doi.org/10.1007/978-3-319-06257-0_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06256-3

  • Online ISBN: 978-3-319-06257-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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