Probability Estimation of Uncertain Process Trace Realizations

TL;DR

This paper introduces a method to accurately estimate the probabilities of different scenarios in uncertain process logs, enhancing the reliability of process mining analyses involving stochastic event data.

Contribution

It presents a novel approach for probability estimation in uncertain event logs, addressing the challenge of non-deterministic event attributes in process mining.

Findings

Probabilities closely match true chances of outcomes

Enables trustworthy analysis of uncertain data

Improves process mining reliability

Abstract

Process mining is a scientific discipline that analyzes event data, often collected in databases called event logs. Recently, uncertain event logs have become of interest, which contain non-deterministic and stochastic event attributes that may represent many possible real-life scenarios. In this paper, we present a method to reliably estimate the probability of each of such scenarios, allowing their analysis. Experiments show that the probabilities calculated with our method closely match the true chances of occurrence of specific outcomes, enabling more trustworthy analyses on uncertain data.