Conformance Checking Over Stochastically Known Logs

TL;DR

This paper introduces a novel approach for conformance checking between process models and event logs that are uncertain and probabilistically modeled, enabling more accurate analysis of stochastic logs.

Contribution

It develops a mathematical framework for stochastic conformance checking, including a stochastic trace model, synchronous product, and cost function, and evaluates it through experiments.

Findings

The stochastic approach effectively handles uncertain event logs.

It outperforms standard alignment-based methods on benchmark datasets.

The method provides a viable tool for analyzing stochastic process data.

Abstract

With the growing number of devices, sensors and digital systems, data logs may become uncertain due to, e.g., sensor reading inaccuracies or incorrect interpretation of readings by processing programs. At times, such uncertainties can be captured stochastically, especially when using probabilistic data classification models. In this work we focus on conformance checking, which compares a process model with an event log, when event logs are stochastically known. Building on existing alignment-based conformance checking fundamentals, we mathematically define a stochastic trace model, a stochastic synchronous product, and a cost function that reflects the uncertainty of events in a log. Then, we search for an optimal alignment over the reachability graph of the stochastic synchronous product for finding an optimal alignment between a model and a stochastic process observation. Via structured experiments with two well-known process mining benchmarks, we explore the behavior of the suggested stochastic conformance checking approach and compare it to a standard alignment-based approach as well as to an approach that creates a lower bound on performance. We envision the proposed stochastic conformance checking approach as a viable process mining component for future analysis of stochastic event logs.