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Mining variable fragments from process event logs

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Abstract

Many peer-organizations are now using process-aware information systems for managing their organizational processes. Most of these peer-organizations have shared processes, which include many commonalities and some degrees of variability. Analyzing and mining the commonalities of these processes can have many benefits from the reusability point of view. In this paper, we propose an approach for extracting common process fragments from a collection of event logs. To this end, we first analyze the process fragment literature from a theoretical point of view, based on which we present a new process fragment definition, called morphological fragments to support composability and flexibility. Then we propose a novel algorithm for extracting such morphological fragments directly from process event logs. This algorithm is capable of eliciting common fragments from a family of processes that may not have been executed within the same application/organization. We also propose supporting algorithms for detecting and categorizing morphological fragments for the purpose of reusability. Our empirical studies show that our approach is able to support reusability and flexibility in process fragment identification.

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Notes

  1. The evaluation was conducted by investigating the coverage of the criteria for each definition. In the cases that there were uncertainties for the coverage, they have been discussed between the authors to reach conclusions about the coverage.

  2. This is the same as the concept of n-grams in computational linguistics but we use the term L-grams as we reserve variable n to represent the number of traces in an event log.

  3. BIT-Process Library-Release 2009

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Authors and Affiliations

  1. Web Technology Laboratory, Ferdowsi University of Mashhad, Mashhad, Iran

    Asef Pourmasoumi & Mohsen Kahani

  2. Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada

    Ebrahim Bagheri

Authors
  1. Asef Pourmasoumi
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  2. Mohsen Kahani
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  3. Ebrahim Bagheri
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Correspondence to Mohsen Kahani.

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Pourmasoumi, A., Kahani, M. & Bagheri, E. Mining variable fragments from process event logs. Inf Syst Front 19, 1423–1443 (2017). https://doi.org/10.1007/s10796-016-9662-x

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