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CoSMo: a Framework to Instantiate Conditioned Process Simulation Models

Rafael S. Oyamada, Gabriel M. Tavares, Sylvio Barbon Junior, Paolo Ceravolo

TL;DR

A novel recurrent neural architecture tailored to discover COnditioned process Simulation MOdels (CoSMo) based on user-based constraints or any other nature of a-priori knowledge that facilitates the simulation of event logs that adhere to specific constraints by incorporating declarative-based rules into the learning phase.

Abstract

Process simulation is gaining attention for its ability to assess potential performance improvements and risks associated with business process changes. The existing literature presents various techniques, generally grounded in process models discovered from event log data or built upon deep learning algorithms. These techniques have specific strengths and limitations. Traditional data-driven approaches offer increased interpretability, while deep learning-based excel at generalizing changes across large event logs. However, the practical application of deep learning faces challenges related to managing stochasticity and integrating information for what-if analysis. This paper introduces a novel recurrent neural architecture tailored to discover COnditioned process Simulation MOdels (CoSMo) based on user-based constraints or any other nature of a-priori knowledge. This architecture facilitates the simulation of event logs that adhere to specific constraints by incorporating declarative-based rules into the learning phase as an attempt to fill the gap of incorporating information into deep learning models to perform what-if analysis. Experimental validation illustrates CoSMo's efficacy in simulating event logs while adhering to predefined declarative conditions, emphasizing both control-flow and data-flow perspectives.

CoSMo: a Framework to Instantiate Conditioned Process Simulation Models

TL;DR

A novel recurrent neural architecture tailored to discover COnditioned process Simulation MOdels (CoSMo) based on user-based constraints or any other nature of a-priori knowledge that facilitates the simulation of event logs that adhere to specific constraints by incorporating declarative-based rules into the learning phase.

Abstract

Process simulation is gaining attention for its ability to assess potential performance improvements and risks associated with business process changes. The existing literature presents various techniques, generally grounded in process models discovered from event log data or built upon deep learning algorithms. These techniques have specific strengths and limitations. Traditional data-driven approaches offer increased interpretability, while deep learning-based excel at generalizing changes across large event logs. However, the practical application of deep learning faces challenges related to managing stochasticity and integrating information for what-if analysis. This paper introduces a novel recurrent neural architecture tailored to discover COnditioned process Simulation MOdels (CoSMo) based on user-based constraints or any other nature of a-priori knowledge. This architecture facilitates the simulation of event logs that adhere to specific constraints by incorporating declarative-based rules into the learning phase as an attempt to fill the gap of incorporating information into deep learning models to perform what-if analysis. Experimental validation illustrates CoSMo's efficacy in simulating event logs while adhering to predefined declarative conditions, emphasizing both control-flow and data-flow perspectives.
Paper Structure (13 sections, 1 equation, 4 figures, 3 tables)

This paper contains 13 sections, 1 equation, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Current State and Limitations of Process Simulation
  4. Conditioned Process Simulation Models
  5. Conditioned Recurrent Architecture
  6. CoSMo Instantiation
  7. Conditioned Simulation
  8. Evaluation
  9. Experimental Setup
  10. Conditioned Simulation of Process Behaviors
  11. Distinctive Advancements and Limitations
  12. Conclusion
  13. Acknowledgments.

Figures (4)

  • Figure 1: Proposed CoSMo framework (left) and the proposed recurrent architecture (right) tailored to capture the relation between user-based constraints and processes.
  • Figure 2: Weighted precision for the simulated logs under the different approaches and DECLARE templates.
  • Figure 3: Analysis of event log constraints from two perspectives: (a) count per log across templates, (b) distribution per template.
  • Figure 4: Data-flow of the proposed CoSMo framework.