Mining Uncertain Event Data in Process Mining

TL;DR

This paper explores the analysis of uncertain event logs in process mining, introducing a taxonomy and methods for handling uncertainty in process discovery and conformance checking.

Contribution

It defines a taxonomy of uncertain event logs and models, and proposes techniques for deriving bounds on conformance in the presence of uncertainty.

Findings

Uncertain logs can be systematically classified and modeled.

Bounds for conformance can be computed using alignment techniques.

The approach enhances process analysis with uncertain data.

Abstract

Nowadays, more and more process data are automatically recorded by information systems, and made available in the form of event logs. Process mining techniques enable process-centric analysis of data, including automatically discovering process models and checking if event data conform to a certain model. In this paper we analyze the previously unexplored setting of uncertain event logs: logs where quantified uncertainty is recorded together with the corresponding data. 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 aligning an uncertain trace onto a regular process model.