Efficient Conformance Checking of Rich Data-Aware Declare Specifications (Extended)

2025-06-30

AI Summary

Existing alignment-based compliance checking approaches support only pure control-flow constraints or simple numerical comparisons, rendering them inadequate for Data-aware Declare specifications involving rich data types and complex data conditions. This paper proposes the first optimal alignment computation framework supporting diverse data types and intricate data constraints. Our method integrates A* heuristic search with SMT solving and introduces a repair-action-driven state evolution mechanism to jointly reason about control flow and data dependencies. It guarantees theoretical optimality while substantially improving computational efficiency. Experimental evaluation demonstrates significantly enhanced expressive power: our approach achieves state-of-the-art or superior performance across multiple benchmarks, validating its effectiveness and scalability in real-world business process scenarios.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Search and Optimization: Heuristic Search
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Despite growing interest in process analysis and mining for data-aware specifications, alignment-based conformance checking for declarative process models has focused on pure control-flow specifications, or mild data-aware extensions limited to numerical data and variable-to-constant comparisons. This is not surprising: finding alignments is computationally hard, even more so in the presence of data dependencies. In this paper, we challenge this problem in the case where the reference model is captured using data-aware Declare with general data types and data conditions. We show that, unexpectedly, it is possible to compute data-aware optimal alignments in this rich setting, enjoying at once efficiency and expressiveness. This is achieved by carefully combining the two best-known approaches to deal with control flow and data dependencies when computing alignments, namely A* search and SMT solving. Specifically, we introduce a novel algorithmic technique that efficiently explores the search space, generating descendant states through the application of repair actions aiming at incrementally resolving constraint violations. We prove the correctness of our algorithm and experimentally show its efficiency. The evaluation witnesses that our approach matches or surpasses the performance of the state of the art while also supporting significantly more expressive data dependencies, showcasing its potential to support real-world applications.

Problem

• Extends alignment-based conformance checking to rich data-aware Declare models
• Combines A* search and SMT solving for efficient optimal alignments
• Supports general data types and complex data conditions

Innovation

• Combines A* search and SMT solving
• Efficiently explores search space with repair actions
• Supports expressive data dependencies efficiently