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Bridging Imperative Process Models and Process Data Queries-Translation and Relaxation

Abdur Rehman Anwar Qureshi, Adrian Rebmann, Timotheus Kampik, Matthias Weidlich, Mathias Weske

TL;DR

The paper addresses the gap between traditional imperative process models and data-driven analysis by translating sound, free-choice WF-nets into declarative constraints that map to executable SQL queries for conformance checking. It proposes a three-step pipeline: (i) derive directly-follows and eventually-follows relations via MSS to preserve trace semantics, (ii) allow user-driven relaxations of these relations to reflect real-world flexibility, and (iii) generate declarative constraints that are convertible to SQL (via MATCH_RECOGNIZE) for efficient querying over event logs. A proof-of-concept on BPIC19 demonstrates that relaxing constraints substantially reduces false positives, improving conformance signals from $0.691$ to $0.823$ in the studied case. The approach highlights the continued relevance of imperative models while leveraging declarative constraints for scalable, data-driven conformance analysis, and lays groundwork for broader relaxation techniques and user-guided tooling in process querying.

Abstract

Business process management is increasingly practiced using data-driven approaches. Still, classical imperative process models, which are typically formalized using Petri nets, are not straightforwardly applicable to the relational databases that contain much of the available structured process execution data. This creates a gap between the traditional world of process modeling and recent developments around data-driven process analysis, ultimately leading to the under-utilization of often readily available process models. In this paper, we close this gap by providing an approach for translating imperative models into relaxed process data queries, specifically SQL queries executable on relational databases, for conformance checking. Our results show the continued relevance of imperative process models to data-driven process management, as well as the importance of behavioral footprints and other declarative approaches for integrating model-based and data-driven process management.

Bridging Imperative Process Models and Process Data Queries-Translation and Relaxation

TL;DR

The paper addresses the gap between traditional imperative process models and data-driven analysis by translating sound, free-choice WF-nets into declarative constraints that map to executable SQL queries for conformance checking. It proposes a three-step pipeline: (i) derive directly-follows and eventually-follows relations via MSS to preserve trace semantics, (ii) allow user-driven relaxations of these relations to reflect real-world flexibility, and (iii) generate declarative constraints that are convertible to SQL (via MATCH_RECOGNIZE) for efficient querying over event logs. A proof-of-concept on BPIC19 demonstrates that relaxing constraints substantially reduces false positives, improving conformance signals from to in the studied case. The approach highlights the continued relevance of imperative models while leveraging declarative constraints for scalable, data-driven conformance analysis, and lays groundwork for broader relaxation techniques and user-guided tooling in process querying.

Abstract

Business process management is increasingly practiced using data-driven approaches. Still, classical imperative process models, which are typically formalized using Petri nets, are not straightforwardly applicable to the relational databases that contain much of the available structured process execution data. This creates a gap between the traditional world of process modeling and recent developments around data-driven process analysis, ultimately leading to the under-utilization of often readily available process models. In this paper, we close this gap by providing an approach for translating imperative models into relaxed process data queries, specifically SQL queries executable on relational databases, for conformance checking. Our results show the continued relevance of imperative process models to data-driven process management, as well as the importance of behavioral footprints and other declarative approaches for integrating model-based and data-driven process management.

Paper Structure

This paper contains 11 sections, 2 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Motivating Example
  3. Preliminaries
  4. Approach
  5. Approach Overview
  6. Step 1: Determine Behavioral Relations
  7. Step 2: Relax Behavioral Relations
  8. Step 3: Generating Declarative Constraints
  9. Proof of Concept
  10. Related Work
  11. Conclusion

Figures (2)

  • Figure 1: An exemplary BPMN process model of a procurement process.
  • Figure 2: Approach overview.