Stochastic Process Mining: Characteristics and Challenges
IEEE Transactions on Knowledge and Data Engineering, vol. 38, pp. 7061–7079
Abstract
Process mining aims to obtain insights from event logs through the automated analyses of recorded process data in information systems, with the ultimate aim to improve business processes running in organisations. However, real-life event logs are often incomplete, noisy, or ambiguous, such as missing timestamps or having ambiguous event labels, which traditional deterministic models cannot capture. Recent process mining developments have considered uncertainty in process mining artifacts more explicitly: in logs of recorded process behaviour, uncertainty may implicitly or explicitly influence process mining outcomes, while in process models, explicit uncertainty allows analysts to interpret and value outcomes. In this paper, we provide a conceptual foundation for uncertainty in process mining by introducing a four-level specification that separately addresses uncertainty in log attributes (e.g., activity labels of events, frequencies) and model elements (e.g., service times, read guards). For each type of uncertainty, we illustrate the levels with concrete examples to help understanding and application. We then provide a structured overview of the state of the art in stochastic process mining, classified using our specification, and present key open research challenges.
Authors 22
-
Sander J. J. Leemans Aachen
Affiliation as printed
RWTH Aachen University
-
Affiliation as printed
Utrecht University
-
Affiliation as printed
University of Tartu
-
Affiliation as printed
Utrecht University
-
Affiliation as printed
Bar-Ilan University
-
Affiliation as printed
Hasselt University
-
Affiliation as printed
University of Liechtenstein
-
Technion – Israel Institute of Technology
Affiliation as printed
Technion – Israel Institute of Technology
-
Affiliation as printed
University of Padua
-
Affiliation as printed
Kühne Logistics University
-
Affiliation as printed
University of Tartu
-
Affiliation as printed
SINTEF Digital
-
Free University of Bozen-Bolzano
Affiliation as printed
Free University of Bozen-Bolzano
-
Affiliation as printed
Hasselt University
-
Free University of Bozen-Bolzano
Affiliation as printed
Free University of Bozen-Bolzano
-
Marco Pegoraro Aachen
Affiliation as printed
RWTH Aachen University
-
Affiliation as printed
University of Melbourne
-
Affiliation as printed
York University
-
Affiliation as printed
Vrije Universiteit Brussel
-
Affiliation as printed
NTT Corporation
-
Affiliation as printed
Hasso Plattner Institute
-
Affiliation as printed
Utrecht University
Cited by 0 stored of 0
No patents citing this paper on Lens.org (checked 2026-10-06).