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Online Process Discovery to Detect Concept Drifts in LTL-Based Declarative Process Models

  • Conference paper
On the Move to Meaningful Internet Systems: OTM 2013 Conferences (OTM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8185))

Abstract

Today’s business processes are often controlled and supported by information systems. These systems record real-time information about business processes during their executions. This enables the analysis at runtime of the process behavior. However, many modern systems produce “big data”, i.e., collections of data sets so large and complex that it becomes impossible to store and process all of them. Moreover, few processes are in steady-state and due to changing circumstances processes evolve and systems need to adapt continuously. In this paper, we present a novel framework for the discovery of LTL-based declarative process models from streaming event data in settings where it is impossible to store all events over an extended period or where processes evolve while being analyzed. The framework continuously updates a set of valid business constraints based on the events occurred in the event stream. In addition, our approach is able to provide meaningful information about the most significant concept drifts, i.e., changes occurring in a process during its execution. We report about experimental results obtained using logs pertaining the health insurance claims handling in a travel agency.

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Author information

Authors and Affiliations

  1. University of Tartu, Estonia

    Fabrizio Maria Maggi

  2. University of Padova, Italy

    Andrea Burattin & Alessandro Sperduti

  3. Unitelma Sapienza University, Italy

    Marta Cimitile

Authors
  1. Fabrizio Maria Maggi
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  2. Andrea Burattin
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  3. Marta Cimitile
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  4. Alessandro Sperduti
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Editor information

Editors and Affiliations

  1. STAR Lab, Vrije Universiteit Brussel, Bldg G/10, Pleinlaan 2, 1050, Brussels, Belgium

    Robert Meersman

  2. CRAN, University of Lorraine, Campus Sciences, BP 70239, 54506, Vandoevre-les-Nancy, France

    Hervé Panetto

  3. Computer Science and Computer Engineering, La Trobe University, 3086, Melbourne, Victoria, Australia

    Tharam Dillon

  4. Dept. of Informatics Systems, University of Klagenfurt, Universitaetsstrasse 65, 9020, Klagenfurt, Austria

    Johann Eder

  5. LIRMM, University of Montpellier II, 161 Rue Ada, 34392, Montpellier Cedex 5, France

    Zohra Bellahsene

  6. Department of Informatics, University of Hamburg, 22527, Hamburg, Germany

    Norbert Ritter

  7. Department of Computer Sciences, VU University Amsterdam, De Boelelaan 1081, 1081 HV, Amsterdam, The Netherlands

    Pieter De Leenheer

  8. Computer and Information Science Department, University of Oregon, 97403, Eugene, OR, USA

    Deijing Dou

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Maggi, F.M., Burattin, A., Cimitile, M., Sperduti, A. (2013). Online Process Discovery to Detect Concept Drifts in LTL-Based Declarative Process Models. In: Meersman, R., et al. On the Move to Meaningful Internet Systems: OTM 2013 Conferences. OTM 2013. Lecture Notes in Computer Science, vol 8185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41030-7_7

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  • DOI: https://doi.org/10.1007/978-3-642-41030-7_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41029-1

  • Online ISBN: 978-3-642-41030-7

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