Conformance Checking of Fuzzy Logs against Declarative Temporal Specifications

TL;DR

This paper introduces a fuzzy logic-based approach for conformance checking of event logs with uncertainty, enabling verification of process compliance against declarative temporal rules in a probabilistic setting.

Contribution

It develops a fuzzy extension of LTLf, formulates conformance checking as a verification problem, and provides an efficient implementation using PyTorch for handling fuzzy logs.

Findings

Fuzzy LTLf effectively models uncertainty in event data.

The approach allows simultaneous checking of multiple fuzzy traces.

Implementation demonstrates practical efficiency and scalability.

Abstract

Traditional conformance checking tasks assume that event data provide a faithful and complete representation of the actual process executions. This assumption has been recently questioned: more and more often events are not traced explicitly, but are instead indirectly obtained as the result of event recognition pipelines, and thus inherently come with uncertainty. In this work, differently from the typical probabilistic interpretation of uncertainty, we consider the relevant case where uncertainty refers to which activity is actually conducted, under a fuzzy semantics. In this novel setting, we consider the problem of checking whether fuzzy event data conform with declarative temporal rules specified as Declare patterns or, more generally, as formulae of linear temporal logic over finite traces (LTLf). This requires to relax the assumption that at each instant only one activity is executed, and to correspondingly redefine boolean operators of the logic with a fuzzy semantics. Specifically, we provide a threefold contribution. First, we define a fuzzy counterpart of LTLf tailored to our purpose. Second, we cast conformance checking over fuzzy logs as a verification problem in this logic. Third, we provide a proof-of-concept, efficient implementation based on the PyTorch Python library, suited to check conformance of multiple fuzzy traces at once.