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Metric Trees for Efficient Similarity Search in Large Process Model Repositories

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Business Process Management Workshops (BPM 2010)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 66))

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Summary

Due to the increasing adoption of business process management and the key role of process models, companies are setting up and maintaining large process model repositories. Repositories containing hundreds or thousands of process models are not uncommon, whereas only simplistic search functionality, such as text based search or folder navigation, is provided, today.

On the other hand, advanced methods have recently been proposed in the literature to ascertain the similarity of process models. However, due to performance reasons, an exhaustive similarity search by pairwise comparison is not feasible in large process model repositories.

This paper presents an indexing approach based on metric trees, a hierarchical search structure that saves comparison operations during search with nothing but a distance function at hand. A detailed investigation of this approach is provided along with a quantitative evaluation thereof, showing its suitability and scalability in large process model repositories.

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Author information

Authors and Affiliations

  1. Hasso Plattner Institute, University of Potsdam, Prof.-Dr.-Helmert-Strae 2-3, 14482, Potsdam, Germany

    Matthias Kunze & Mathias Weske

Authors
  1. Matthias Kunze
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  2. Mathias Weske
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Editors and Affiliations

  1. Stevens Institute of Technology, Howe School of Technology Management, 07030, Hoboken, NY, USA

    Michael zur Muehlen

  2. Department of Computer Science, University of California, 93106-5110, Santa Barbara, CA, USA

    Jianwen Su

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Kunze, M., Weske, M. (2011). Metric Trees for Efficient Similarity Search in Large Process Model Repositories. In: zur Muehlen, M., Su, J. (eds) Business Process Management Workshops. BPM 2010. Lecture Notes in Business Information Processing, vol 66. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20511-8_49

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  • DOI: https://doi.org/10.1007/978-3-642-20511-8_49

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20510-1

  • Online ISBN: 978-3-642-20511-8

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