Probabilistic and Non-Deterministic Event Data in Process Mining: Embedding Uncertainty in Process Analysis Techniques

TL;DR

This paper explores the integration of uncertain event data into process mining, addressing the challenges of imprecision in event logs and proposing methods to incorporate uncertainty into process analysis techniques.

Contribution

It provides a comprehensive overview of uncertain event data, reviews current approaches, and discusses open challenges in embedding uncertainty into process mining.

Findings

Identifies key types of uncertain event data.

Highlights current methods for handling uncertainty.

Outlines open research challenges in the field.

Abstract

Process mining is a subfield of process science that analyzes event data collected in databases called event logs. Recently, novel types of event data have become of interest due to the wide industrial application of process mining analyses. In this paper, we examine uncertain event data. Such data contain meta-attributes describing the amount of imprecision tied with attributes recorded in an event log. We provide examples of uncertain event data, present the state of the art in regard of uncertainty in process mining, and illustrate open challenges related to this research direction.