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International Conference on Conceptual Modeling

ER 2015: Conceptual Modeling pp 514–527Cite as

Mining Process Task Post-Conditions

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9381))

Abstract

A large and growing body of work explores the use of semantic annotation of business process designs, but these annotations can be difficult and expensive to acquire. This paper presents a data-driven approach to mining these annotations (and specifically post-conditions) from event logs in process execution histories which describe both task execution events (typically contained in process logs) and state update events (which we record in effect logs). We present an empirical evaluation, which suggests that the approach provides generally reliable results.

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Author information

Authors and Affiliations

  1. Decision Systems Lab, School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, 2522, Australia

    Metta Santiputri, Aditya K. Ghose, Hoa Khanh Dam & Xiong Wen

Authors
  1. Metta Santiputri
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  2. Aditya K. Ghose
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  3. Hoa Khanh Dam
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  4. Xiong Wen
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Corresponding author

Correspondence to Metta Santiputri .

Editor information

Editors and Affiliations

  1. Stockholm University, Kista, Sweden

    Paul Johannesson

  2. National University of Singapore, Singapore, Singapore

    Mong Li Lee

  3. Brigham Young University, Provo, Utah, USA

    Stephen W. Liddle

  4. University of Bergen, Bergen, Norway

    Andreas L. Opdahl

  5. Universidad Politécnica de Valencia, Valencia, Spain

    Óscar Pastor López

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Santiputri, M., Ghose, A.K., Dam, H.K., Wen, X. (2015). Mining Process Task Post-Conditions. In: Johannesson, P., Lee, M., Liddle, S., Opdahl, A., Pastor López, Ó. (eds) Conceptual Modeling. ER 2015. Lecture Notes in Computer Science(), vol 9381. Springer, Cham. https://doi.org/10.1007/978-3-319-25264-3_38

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  • DOI: https://doi.org/10.1007/978-3-319-25264-3_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25263-6

  • Online ISBN: 978-3-319-25264-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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