Probability Estimation of Uncertain Process Trace Realizations
This work addresses the need for reliable probability estimation in process mining to analyze uncertain event data, representing an incremental improvement in handling stochastic attributes.
The paper tackles the problem of estimating probabilities for different possible scenarios in uncertain event logs, which contain non-deterministic and stochastic event attributes, and shows that the method's calculated probabilities closely match true occurrence chances, enabling more trustworthy analyses.
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.