Object-centric Processes with Structured Data and Exact Synchronization (Extended Version)

2025-05-21

AI Summary

Existing process modeling approaches struggle to simultaneously support object identity tracking, complex data type handling, inter-object dependency representation, and object-aware synchronization. While Object-Centered Petri Nets (OPNs) partially address these challenges, they model objects as unstructured identifiers, thereby failing to capture rich data semantics. This paper introduces Data-Aware Object-Centered Petri Nets (DOPNs), the first formal extension of OPNs that natively supports structured data operations and full object-aware synchronization within a rigorous mathematical framework. We further propose an SMT-based data-aware alignment algorithm that integrates process mining with formal verification, enabling decidable alignment computation and compliance checking under complex data constraints. DOPNs strike a critical balance between expressive power and operational tractability, significantly enhancing modeling fidelity and analytical capabilities for dynamic, object-intensive business processes.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Knowledge Representation and Reasoning: Description Logics
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• 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: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Real-world processes often involve interdependent objects that also carry data values, such as integers, reals, or strings. However, existing process formalisms fall short to combine key modeling features, such as tracking object identities, supporting complex datatypes, handling dependencies among them, and object-aware synchronization. Object-centric Petri nets with identifiers (OPIDs) partially address these needs but treat objects as unstructured identifiers (e.g., order and item IDs), overlooking the rich semantics of complex data values (e.g., item prices or other attributes). To overcome these limitations, we introduce data-aware OPIDs (DOPIDs), a framework that strictly extends OPIDs by incorporating structured data manipulation capabilities, and full synchronization mechanisms. In spite of the expressiveness of the model, we show that it can be made operational: Specifically, we define a novel conformance checking approach leveraging satisfiability modulo theories (SMT) to compute data-aware object-centric alignments.

Problem

• Modeling interdependent objects with complex data values
• Enhancing object-centric processes with structured data manipulation
• Enabling exact synchronization for data-aware object-centric alignments

Innovation

• Extends OPIDs with structured data manipulation
• Introduces full synchronization mechanisms
• Uses SMT for data-aware conformance checking