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International Conference on Research Challenges in Information Science

RCIS 2020: Research Challenges in Information Science pp 417–433Cite as

Incremental Discovery of Hierarchical Process Models

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 385))

Abstract

Many of today’s information systems record the execution of (business) processes in great detail. Process mining utilizes such data and aims to extract valuable insights. Process discovery, a key research area in process mining, deals with the construction of process models based on recorded process behavior. Existing process discovery algorithms aim to provide a “push-button-technology”, i.e., the algorithms discover a process model in a completely automated fashion. However, real data often contain noisy and/or infrequent complex behavioral patterns. As a result, the incorporation of all behavior leads to very imprecise or overly complex process models. At the same time, data pre-processing techniques have shown to be able to improve the precision of process models, i.e., without explicitly using domain knowledge. Yet, to obtain superior process discovery results, human input is still required. Therefore, we propose a discovery algorithm that allows a user to incrementally extend a process model by new behavior. The proposed algorithm is designed to localize and repair nonconforming process model parts by exploiting the hierarchical structure of the given process model. The evaluation shows that the process models obtained with our algorithm, which allows for incremental extension of a process model, have, in many cases, superior characteristics in comparison to process models obtained by using existing process discovery and model repair techniques.

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Notes

  1. 1.

    For example, the Inductive Miner algorithm [16] fulfills the listed requirements.

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Authors and Affiliations

  1. Fraunhofer Institute for Applied Information Technology FIT, Sankt Augustin, Germany

    Daniel Schuster, Sebastiaan J. van Zelst & Wil M. P. van der Aalst

  2. RWTH Aachen University, Aachen, Germany

    Sebastiaan J. van Zelst & Wil M. P. van der Aalst

Authors
  1. Daniel Schuster
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  2. Sebastiaan J. van Zelst
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  3. Wil M. P. van der Aalst
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Corresponding author

Correspondence to Daniel Schuster .

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Editors and Affiliations

  1. Utrecht University, Utrecht, The Netherlands

    Fabiano Dalpiaz

  2. Stockholm University, Kista, Sweden

    Jelena Zdravkovic

  3. The Institute of Digital Innovation and Research, Dublin, Ireland

    Pericles Loucopoulos

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Schuster, D., van Zelst, S.J., van der Aalst, W.M.P. (2020). Incremental Discovery of Hierarchical Process Models. In: Dalpiaz, F., Zdravkovic, J., Loucopoulos, P. (eds) Research Challenges in Information Science. RCIS 2020. Lecture Notes in Business Information Processing, vol 385. Springer, Cham. https://doi.org/10.1007/978-3-030-50316-1_25

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  • DOI: https://doi.org/10.1007/978-3-030-50316-1_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50315-4

  • Online ISBN: 978-3-030-50316-1

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