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

arXiv (Cornell University)

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.

Authors 1

1. Marco Pegoraro corresponding Aachen Chair of Process and Data Science (PADS) Department of Computer Science

   RWTH Aachen University

   Affiliation as printed

   Chair of Process and Data Science (PADS ) Department of Computer Science , RWTH Aachen University , Aachen , Germany