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Related Experiment Videos

Discovering generative models from event logs: data-driven simulation vs deep learning.

Manuel Camargo1,2, Marlon Dumas1, Oscar González-Rojas2

  • 1Institute of Computer Science, University of Tartu, Tartu, Estonia.

Peerj. Computer Science
|July 29, 2021
PubMed
Summary
This summary is machine-generated.

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This study compares data-driven simulation and deep learning generative business process models. It reveals their distinct strengths, paving the way for advanced hybrid approaches.

Area of Science:

  • Business Process Management
  • Artificial Intelligence
  • Data Science

Background:

  • Generative models create new data instances from observed data.
  • In business processes, generative models produce new execution traces from event logs.
  • Existing generative business process models include data-driven simulation and deep learning approaches, which have developed independently.

Purpose of the Study:

  • To empirically compare the performance of data-driven simulation and deep learning approaches for generative business process modeling.
  • To identify the relative strengths and weaknesses of these two distinct modeling paradigms.
  • To explore the potential for developing hybrid generative business process models.

Main Methods:

  • Comparative empirical study of generative business process modeling techniques.
Keywords:
Data-driven simulationDeep learningProcess mining

Related Experiment Videos

  • Implementation and evaluation of a data-driven simulation model.
  • Implementation and evaluation of multiple deep learning models for generative business process modeling.

    • Main Results:

    • The study provides an empirical comparison of data-driven simulation and deep learning methods for generative business process models.
    • The relative performance and specific strengths of each approach are elucidated.
    • Findings highlight the potential for novel hybrid approaches combining the benefits of both methods.

    Conclusions:

    • Data-driven simulation and deep learning models offer different strengths for generative business process modeling.
    • Empirical comparison is crucial for understanding these differences.
    • Hybrid approaches hold promise for advancing generative business process modeling capabilities.