DeclareAligner: A leap towards efficient optimal alignments for declarative process model conformance checking

Conformance checking is a crucial aspect of process mining, enabling organizations to identify deviations between actual process behavior and modeled expectations. At the heart of conformance checking lies the concept of optimal alignments, which provide a detailed, cost-minimized mapping of observe...

Full description

Bibliographic Details
Authors: Casas Ramos, Jacobo, Lama Penín, Manuel, Mucientes Molina, Manuel
Format: article
Publication Date:2025
Country:España
Institution:Universidad de Santiago de Compostela (USC)
Repository:Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela
Language:English
OAI Identifier:oai:minerva.usc.gal:10347/43349
Online Access:https://hdl.handle.net/10347/43349
Access Level:Open access
Keyword:Process mining
Conformance checking
Optimal alignments
Declarative process models
120304 Inteligencia artificial
Description
Summary:Conformance checking is a crucial aspect of process mining, enabling organizations to identify deviations between actual process behavior and modeled expectations. At the heart of conformance checking lies the concept of optimal alignments, which provide a detailed, cost-minimized mapping of observed behavior to expected behavior. Optimal alignments facilitate the identification of root causes of non-conformity and guide corrective actions. 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 declarative process 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 8054 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.