Alloy-Driven Verification of Object-Centric Event Data: From Temporal Logic to Knowledge Graphs

2025-11-10

AI Summary

Traditional processing of Object-Centered Event Data (OCED) suffers from semantic loss, obscured inter-object relationships, and meta-model inconsistency due to flattening. To address these issues, this paper proposes FOCED—a formal framework built on Alloy for rigorous meta-model verification. FOCED precisely specifies cross-object cardinality constraints and temporal consistency rules, ensuring both correctness of the OCED meta-model and preservation of native semantics. It innovatively applies Alloy to event data modeling, enabling semantic-faithful mapping from temporal logic to knowledge graphs. A Python-based toolchain automatically generates Alloy binding interfaces and integrates with graph databases (e.g., Neo4j), facilitating industrial-scale discovery and validation of implicit dependencies in event logs. Open-sourced and empirically validated across multiple real-world scenarios, FOCED significantly improves data completeness, observability, and query optimization in knowledge graph construction.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Description Logics
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• 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
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Object-centric process mining addresses the limitations of traditional approaches, which often involve the lossy flattening of event data and obscure vital relationships among interacting objects. This paper presents a novel formal framework for Object-centric Event Data (OCED) that ensures the correctness of the meta-model and preserves native object-centric semantics prior to the system implementation. Our approach effectively leverages Alloy for precisely specifying temporal properties and structural relationships between objects and events. This guarantees thorough verification against predefined OCED constraints such as cross-object cardinality bounds and time-aware consistency rules, hence preventing common data integrity issues. We demonstrate the effectiveness of the proposed framework in discovering and validating implicit object dependencies in event logs, particularly when importing data into graph databases like Neo4j. This demonstrates how formal verification can avoid pitfalls that lead to data invisibility and improve knowledge graph creation, enrichment, and querying. To bridge theory and practice, our verified emph{FOCED} is made accessible through automatically generated Python bindings, empowering industrial users without formal methods expertise. The code is available on GitHub footnote{https://github.com/sabalati/FOCED}

Problem

• Verifying object-centric event data correctness before system implementation
• Ensuring temporal and structural integrity across interacting objects and events
• Preventing data invisibility and improving knowledge graph creation processes

Innovation

• Alloy verifies temporal properties and structural relationships
• Ensures cross-object cardinality bounds and time-aware consistency
• Generates Python bindings for practical industrial application