Towards an Empirical Evaluation of Imperative and Declarative Process Mining

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11158)


Process modelling notations fall in two broad categories: declarative notations, which specify the rules governing a process; and imperative notations, which specify the flows admitted by a process. We outline an empirical approach to addressing the question of whether certain process logs are better suited for mining to imperative than declarative notations. We plan to attack this question by applying a flagship imperative and declarative miner to a standard collection of process logs, then evaluate the quality of the output models w.r.t. the standard model metrics of precision and generalisation. This approach requires perfect fitness of the output model, which substantially narrows the field of available miners; possible candidates include Inductive Miner and MINERful. With the metrics in hand, we propose to statistically evaluate the hypotheses that (1) one miner consistently outperforms the other on one of the metrics, and (2) there exist subsets of logs more suitable for imperative respectively declarative mining.


Process mining Modelling paradigms Statistical evaluation Declarative models Imperative models Hybrid models Evaluation metrics 


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of CopenhagenCopenhagen SDenmark
  2. 2.Department of Computer ScienceIT University of CopenhagenCopenhagen SDenmark

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