Aggregating Causal Runs into Workflow Nets

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7400)


This paper provides three aggregation algorithms for deriving system nets from sets of partially-ordered causal runs. The three algorithms differ with respect to the assumptions about the information contained in the causal runs. Specifically, we look at the situations where labels of conditions (i.e. references to places) or events (i.e. references to transitions) are unknown. Since the paper focuses on aggregation in the context of process mining, we solely look at workflow nets, i.e. a class of Petri nets with unique start and end places. The difference of the work presented here and most work on process mining is the assumption that events are logged as partial orders instead of linear traces. Although the work is inspired by applications in the process mining and workflow domains, the results are generic and can be applied in other application domains.


Condition Graph Condition Coloring Connected Subgraph Label Function Condition Label 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceTechnische Universiteit EindhovenThe Netherlands
  2. 2.Department of Software EngineeringFernUniversität in HagenGermany

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