Partial-order-based process mining: a survey and outlook

Sander J. J. Leemans*, Sebastiaan J. van Zelst*, Xixi Lu*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review


The field of process mining focuses on distilling knowledge of the (historical) execution of a
process based on the operational event data generated and stored during its execution. Most
existing process mining techniques assume that the event data describe activity executions
as degenerate time intervals, i.e., intervals of the form [t, t], yielding a strict total order on
the observed activity instances. However, for various practical use cases, e.g., the logging of
activity executions with a nonzero duration and uncertainty on the correctness of the recorded
timestamps of the activity executions, assuming a partial order on the observed activity
instances is more appropriate. Using partial orders to represent process executions, i.e., based
on recorded event data, allows for new classes of process mining algorithms, i.e., aware of
parallelism and robust to uncertainty. Yet, interestingly, only a limited number of studies
consider using intermediate data abstractions that explicitly assume a partial order over a
collection of observed activity instances. Considering recent developments in process mining,
e.g., the prevalence of high-quality event data and techniques for event data abstraction, the
need for algorithms designed to handle partially ordered event data is expected to grow in the
upcoming years. Therefore, this paper presents a survey of process mining techniques that
explicitly use partial orders to represent recorded process behavior. We performed a keyword
search, followed by a snowball sampling strategy, yielding 68 relevant articles in the field.
We observe a recent uptake in works covering partial-order-based process mining, e.g., due
to the current trend of process mining based on uncertain event data. Furthermore, we outline
promising novel research directions for the use of partial orders in the context of process
mining algorithms.
Original languageEnglish
Pages (from-to)1-29
Number of pages29
JournalKnowledge and Information Systems
Issue number1
Publication statusPublished - Jan 2023


  • Process mining
  • Event data
  • Partial orders
  • Survey


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