Probability Estimation of Uncertain Process Trace Realizations

Lecture notes in business information processing, pp. 21–33

Abstract

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.

Authors 4

1. Marco Pegoraro corresponding Aachen Process and Data Science Group Department of Computer Science

   RWTH Aachen University

   Affiliation as printed

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

2. Bianka Bakullari Aachen Process and Data Science Group Department of Computer Science

   RWTH Aachen University

   Affiliation as printed

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

3. Merih Seran Uysal Aachen Process and Data Science Group Department of Computer Science

   RWTH Aachen University

   Affiliation as printed

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

4. Wil M. P. van der Aalst Aachen Process and Data Science Group Department of Computer Science

   RWTH Aachen University

   Affiliation as printed

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