Conformance Checking over Uncertain Event Data

TL;DR

This paper explores conformance checking in process mining when event logs contain explicit uncertainty in event data, proposing methods to handle imprecision in process analysis.

Contribution

It introduces a taxonomy of uncertain event logs and models, and develops techniques to compute bounds for conformance in the presence of uncertainty.

Findings

Defined a taxonomy for uncertain event logs

Analyzed challenges of uncertainty in process discovery and conformance

Proposed alignment-based bounds for conformance measures

Abstract

The strong impulse to digitize processes and operations in companies and enterprises have resulted in the creation and automatic recording of an increasingly large amount of process data in information systems. These are made available in the form of event logs. Process mining techniques enable the process-centric analysis of data, including automatically discovering process models and checking if event data conform to a given model. In this paper, we analyze the previously unexplored setting of uncertain event logs. In such event logs uncertainty is recorded explicitly, i.e., the time, activity and case of an event may be unclear or imprecise. In this work, we define a taxonomy of uncertain event logs and models, and we examine the challenges that uncertainty poses on process discovery and conformance checking. Finally, we show how upper and lower bounds for conformance can be obtained by aligning an uncertain trace onto a regular process model.