What is…

… Multi… Process Mining?

Multi… Process Mining is an umbrella term for process mining with one of the following characteristics.

  • The analysis can work with and distinguish multiple identifiable objects or entity identifiers in the event data – often explicitly or implicitly related to each other. Think of Order-to-Cash or Purchase-to-Pay processes.
  • The analysis derives features for describing, predicting, or automating a specific case from multiple other “surrounding” cases. Think of call centers, queues, and batching.
  • The analysis generally considers how the behavior of multiple other cases, objects, workers, machines, processes, … impacts the behavior of a specific case of interest.
  • Is one missing? Contact us.

Talking about multi… process mining

The process mining field produced many “multi…” terms for such process mining techniques: multi-dimensional process mining, multi-viewpoint process mining, multi-level process mining, multi-event log process mining. Other terms are artifact-centric process mining and object-centric process mining.

They all have in common that they can be placed and positioned in terms of the following four problem quadrants that are explained in this post on “Multi-Dimensional Process Thinking

Blog posts of the Concepts category explore different ways of describing and explaining these phenomena.

Blog posts of the understanding multi… data category show you that these phenomena exist by discussing properties of various event data sets.


The aim of multiprocessmining.org is to inform and communicate about

  • Use cases that have one of the above “multi…” characteristics and cannot easily be answered with classical process mining solutions.
  • Datasets that have one of the above “multi…” characteristics, such as multiple identifiable objects or entity identifiers in the event data, or originate from a process that has a “multi…” analysis use case.
  • Data models and exchange formats to support “multi…” characteristics or “multi…” analyses.
  • Data processing, querying, and understanding of data with “multi…” characteristics or for “multi…” analyses.
  • Techniques, tools, and solutions for Multi…. Process Mining.
  • Case studies showing the value and effectiveness of Multi…. Process Mining techniques, tools, and solutions.

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