The hidden assumptions and forgotten topics of Process Mining, part 1 (Human Behavior)

Also this year, we are organizing the 3rd International workshop on Event Data and Behavioral Analytics (EdbA’22) at the ICPM’22. Part of our workshop philosophy is to dedicate half of the time to open discussions on the topics of the workshop. The discussion often turns to what makes the problems we are looking at difficult – and it’s usually hidden assumptions and under-researched topics. The first session on “Human Behaviors” surfaced the following topics (for which I managed to record a few short bullet points and questions).

Overview on future research perspective on human behavior in smart data space presented by Yannis Bertrand


  • Identifying decision points matters but is difficult
  • Decision points are needed for example for scoping prediction tasks correctly and identify relevant input features: decision points need to be included as they significantly influence outcomes
  • Decisions are difficult to discern (in human behavior): conscious choice based on clear recorded context conditions vs non-determinism due to lack of clear context
  • decision vs non-determinism: try to find features that could explain a choice (needs a lot of domain knowledge and deep analysis) vs explicitly model uncertainty in the absence of sufficient knowledge/data
  • Decisions in human behavior are also often driven by time-outs
  • Choice to behave differently can also be choice to interrupt work or steps and return later -> this is a different kind of decision
  • Decision support: Decision conditions > may not have enough data -> ask user whether to make decision in particular situation of knowledge is not complete from data


  • Andrea Burratin mentioned: maybe when analyzing human behavior we should rather focus on sub-goal of composition of bigger goals, as goals drive human behavior
  • this would ask us to shift the focus of process mining from activity-level to goal-level
  • interestingly, human activities and business activities differ in the explicitness and structure of goals —> intention mining is an interesting field to explore here
  • Identifying and understanding goals does need us to explore and capture the larger context of process activities (other processes, behaviors going on need to be mined/discovered as well)
  • this raises an interesting question of how to conceptualize a process: (1) Process = behavior happening toward a specific goal vs (2) Process = behavior happening in a specific context
  • Goals also change and goals are conflicting -> which goals is now the most important, urgent —> but also other factors play a role
  • Human actors make take decision to NOT do something or to NOT to facilitate a particular goal
  • Human actors also take actions also based on motivations not considered goals or goals outside the process/organization context

ERP systems as systems human actor-driven systems

  • Can’t we perceive an ERP system not as a process-oriented system but rather as a system where human actors “move forward” by jointly working on a number of related data objects?
  • Actors are free to behave in ERP systems and do various things, the process is what emerges from this
  • This specific view on the process is supported by event knowledge graphs.

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