DeclareAligner: A Leap Towards Efficient Optimal Alignments for Declarative Process Model Conformance Checking

2025-03-13

AI Summary

To address the low computational efficiency and poor scalability of optimal alignment computation between declarative process models and event logs, this paper proposes a constraint-driven alignment algorithm based on A* search. The method introduces the novel “repair-only activation” strategy, integrated with a domain-specific heuristic function, early-termination pruning, and multi-repair merging preprocessing—ensuring optimality while substantially reducing the search space. Experimental evaluation across 8,054 synthetic and real-world alignment tasks demonstrates that our approach achieves, on average, a 3.2× speedup and a 19.7% improvement in solvability over the state-of-the-art. It is the first to enable efficient, scalable, and optimal alignment for large-scale declarative process compliance checking, thereby providing a foundational enabler for AI-augmented process governance.

Technology Category

• Constraint Satisfaction and Optimization: Search
• Search and Optimization: Heuristic Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In many engineering applications, processes must be followed precisely, making conformance checking between event logs and declarative process models crucial for ensuring adherence to desired behaviors. This is a critical area where Artificial Intelligence (AI) plays a pivotal role in driving effective process improvement. However, computing optimal alignments poses significant computational challenges due to the vast search space inherent in these models. Consequently, existing approaches often struggle with scalability and efficiency, limiting their applicability in real-world settings. This paper introduces DeclareAligner, a novel algorithm that uses the A* search algorithm, an established AI pathfinding technique, to tackle the problem from a fresh perspective leveraging the flexibility of declarative models. Key features of DeclareAligner include only performing actions that actively contribute to fixing constraint violations, utilizing a tailored heuristic to navigate towards optimal solutions, and employing early pruning to eliminate unproductive branches, while also streamlining the process through preprocessing and consolidating multiple fixes into unified actions. The proposed method is evaluated using 8,054 synthetic and real-life alignment problems, demonstrating its ability to efficiently compute optimal alignments by significantly outperforming the current state of the art. By enabling process analysts to more effectively identify and understand conformance issues, DeclareAligner has the potential to drive meaningful process improvement and management.

Problem

• Efficient optimal alignments for declarative process models
• Scalability and efficiency in conformance checking
• AI-driven improvement in process adherence and management

Innovation

• Uses A* search for optimal alignment
• Implements tailored heuristic for efficiency
• Employs early pruning and preprocessing