Probabilistic and Non-Deterministic Event Data in Process Mining: Embedding Uncertainty in Process Analysis Techniques
This addresses the need to handle uncertainty in process mining for industrial applications, but it is incremental as it reviews existing work and identifies challenges rather than introducing new methods.
The paper tackles the problem of analyzing uncertain event data in process mining, which includes imprecision in event log attributes, by examining examples, presenting the state of the art, and highlighting open challenges without providing specific results or numbers.
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.