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

2025-10-07

AI Summary

Imperative process models (e.g., Petri nets) exhibit semantic and execution-level incompatibility with structured process data in relational databases, hindering data-driven compliance analysis. To address this, we propose an automated model-to-query translation method that maps Petri net models to relaxed SQL queries, incorporating declarative techniques—such as behavioral footprints—to formally encode process constraints. Our approach ensures semantic traceability while unifying model-driven and data-driven analysis. It enables direct generation of executable, verifiable database queries from formal process models, and is empirically validated on real-world industrial datasets. The core contribution is a computationally grounded bridge between imperative process models and relational data, significantly enhancing the reusability and practical applicability of existing process models in data-intensive, compliance-critical scenarios.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

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.

Problem

• Translating imperative process models into executable SQL queries
• Bridging process modeling and data-driven analysis for conformance checking
• Overcoming under-utilization of process models in relational databases

Innovation

• Translating imperative models into SQL queries
• Relaxing process data queries for databases
• Using behavioral footprints for conformance checking