Process mining techniques bridge the gap between data science and process management by extracting insights into process behavior from event logs. However, real-world process behavior is often stochastic or non-deterministic, and event data are frequently incomplete, noisy, or uncertain. These phenomena affect all stages of process mining, from log preprocessing and process discovery to prediction and simulation.
The goal of the First International Workshop on Stochastics, Uncertainty, and Non-determinism in Process Mining (SUN-PM) is to promote the expansion of existing research by providing a platform to discuss novel techniques, theories, and applications in the realm of process mining when data or models exhibit uncertainties, stochastic characteristics, or partial-order structures. The workshop will host new theoretical contributions in the aforementioned topics, and bring together academic researchers and industry experts to exchange ideas, showcase innovative methods, and identify challenges and future directions.
Accepted full-length papers will be submitted for publication in a volume of the Lecture Notes in Business Information Processing series by Springer.
Topics of Interest
We invite contributions that explore or leverage stochastic, uncertain, or non-deterministic methods, as well as partial-order semantics in process mining. Submitted contributions need to be original and unpublished papers. Topics of interest include, but are not limited to:
- Process discovery
- for partially ordered, probabilistic, or uncertain data, e.g. noisy, or generated by LLMs or stochastic models
- the output model contains an explicit notion of stochastics or uncertainty
- Conformance checking techniques on stochastic models, uncertain logs, or any combination thereof
- Extensions of quality or distance metrics, and KPIs on stochastic models and/or uncertain event logs
- Process simulation
- the influence of stochastic model quality on simulation
- discovery of simulation models,
- quality measures for simulation models
- Approximate, probabilistic or non-deterministic methods for declarative constraint checking
- New types of analyses that have a stochastic, uncertain or non-deterministic flavour
- New ways of modeling stochastic, uncertain, or non-deterministic behavior and related learning approaches
- Non-deterministic modelling, analyses or techniques, such as causality-aware process mining, or statistical tests for process behavior
- Applications or case studies in which stochastics, uncertainty or non-determinism play an important role
Workshop Keywords
Process Mining, Stochastic Process Models, Uncertain Event Data, Non-determinism, Partially-Ordered Event Data