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

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

New region-based algorithms for deriving bounded Petri nets

The theory of regions was introduced in the early nineties as a method to bridge state and event-based models. This paper tackles the problem of deriving a Petri net from a state-based model, using the theory of regions. Some of the restrictions required in the traditional approach are dropped in this paper, together with significant extensions that make the approach applicable in new scenarios. One of these scenarios is Process Mining, where accepting (discovering) additional behavior in the synthesized Petri net is sometimes valued. The algorithmic emphasis used in this paper contributes to the demystification of the theory of regions as been only a good theoretical exercise, opening the door for its application in the industrial domain.Peer ReviewedPostprint (published version

Adding A/Sync Places to the Synthesis Procedure for Whole-Place Operations Nets with Localities

Algorithms and the Foundations of Software technolog

Region-based algorithms for process mining and synthesis of Petri nets

The theory of regions was introduced in the early nineties as a bridge between state-based and event-based specifications. Since then, much attention has been paid to theoretical extensions of this theory, but less advances have appeared in the application domain. This paper provides contributions in both dimensions: the theory of bisimulation-based synthesis from Cortadella {em et al.} is generalized and adapted to the area of Process Mining. On the application domain, efficient methods and data structures to support the synthesis problem are developed, together with a practical implementation. Experiments reported witness the practicality of the approach described in this paper.Postprint (published version

Recent advances in petri nets and concurrency

CEUR Workshop Proceeding