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A systematic literature review on state-of-the-art deep learning methods for process prediction

The Artificial intelligence review, 2022-02, Vol.55 (2), p.801-827 [Peer Reviewed Journal]

The Author(s) 2021 ;COPYRIGHT 2022 Springer ;The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0269-2821 ;EISSN: 1573-7462 ;DOI: 10.1007/s10462-021-09960-8

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  • Title:
    A systematic literature review on state-of-the-art deep learning methods for process prediction
  • Author: Neu, Dominic A. ; Lahann, Johannes ; Fettke, Peter
  • Subjects: Algorithms ; Analysis ; Artificial Intelligence ; Computational linguistics ; Computer Science ; Data mining ; Data processing ; Deep learning ; Language processing ; Literature reviews ; Machine learning ; Management science ; Natural language interfaces ; Network topologies ; State-of-the-art reviews ; Systematic review
  • Is Part Of: The Artificial intelligence review, 2022-02, Vol.55 (2), p.801-827
  • Description: Process mining enables the reconstruction and evaluation of business processes based on digital traces in IT systems. An increasingly important technique in this context is process prediction. Given a sequence of events of an ongoing trace, process prediction allows forecasting upcoming events or performance measurements. In recent years, multiple process prediction approaches have been proposed, applying different data processing schemes and prediction algorithms. This study focuses on deep learning algorithms since they seem to outperform their machine learning alternatives consistently. Whilst having a common learning algorithm, they use different data preprocessing techniques, implement a variety of network topologies and focus on various goals such as outcome prediction, time prediction or control-flow prediction. Additionally, the set of log-data, evaluation metrics and baselines used by the authors diverge, making the results hard to compare. This paper attempts to synthesise the advantages and disadvantages of the procedural decisions in these approaches by conducting a systematic literature review.
  • Publisher: Dordrecht: Springer Netherlands
  • Language: English
  • Identifier: ISSN: 0269-2821
    EISSN: 1573-7462
    DOI: 10.1007/s10462-021-09960-8
  • Source: ProQuest One Psychology
    AUTh Library subscriptions: ProQuest Central
    Springer OA刊
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