Validating Temporal Compliance Patterns: A Unified Approach with $MTL_f$ over various Data Models

TL;DR

This paper introduces a unified formal approach using Metric Temporal Logic over finite traces ($MTL_{f}$) for process compliance checking, capturing explicit time constraints and demonstrating effectiveness across various data models and real event logs.

Contribution

It proposes a universal $MTL_{f}$-based framework for compliance rules, including a minimal set of formulas capable of representing common compliance patterns, and maps these to different event log data models.

Findings

Effective encoding of compliance rules using $MTL_{f}$.

Successful application across relational and graph data models.

Empirical validation on real-life event logs confirms approach effectiveness.

Abstract

Process mining extracts valuable insights from event data to help organizations improve their business processes, which is essential for their growth and success. By leveraging process mining techniques, organizations gain a comprehensive understanding of their processes' execution, enabling the discovery of process models, detection of deviations, identification of bottlenecks, and assessment of performance. Compliance checking, a specific area within conformance checking, ensures that the organizational activities adhere to prescribed process models and regulations. Linear Temporal Logic over finite traces ($LTL_{f}$ ) is commonly used for conformance checking, but it may not capture all temporal aspects accurately. This paper proposes Metric Temporal Logic over finite traces ($MTL_{f}$ ) to define explicit time-related constraints effectively in addition to the implicit time-ordering covered by $LTL_f$. Therefore, it provides a universal formal approach to capture compliance rules. Moreover, we define a minimal set of generic $MTL_f$ formulas and show that they are capable of capturing all the common patterns for compliance rules. As compliance validation is largely driven by the data model used to represent the event logs, we provide a mapping from $MTL_f$ to the common data models we found in the literature to encode event logs, namely, the relational and the graph models. A comprehensive study comparing various data models and an empirical evaluation across real-life event logs demonstrates the effectiveness of the proposed approach.