Determining process model precision and generalization with weighted artificial negative events


Autoria(s): Vanden Broucke, S.K.L.M; De Weerdt, J.; Vanthienen , J.; Baesens, B.
Data(s)

2014

Resumo

Process mining encompasses the research area which is concerned with knowledge discovery from event logs. One common process mining task focuses on conformance checking, comparing discovered or designed process models with actual real-life behavior as captured in event logs in order to assess the “goodness” of the process model. This paper introduces a novel conformance checking method to measure how well a process model performs in terms of precision and generalization with respect to the actual executions of a process as recorded in an event log. Our approach differs from related work in the sense that we apply the concept of so-called weighted artificial negative events towards conformance checking, leading to more robust results, especially when dealing with less complete event logs that only contain a subset of all possible process execution behavior. In addition, our technique offers a novel way to estimate a process model’s ability to generalize. Existing literature has focused mainly on the fitness (recall) and precision (appropriateness) of process models, whereas generalization has been much more difficult to estimate. The described algorithms are implemented in a number of ProM plugins, and a Petri net conformance checking tool was developed to inspect process model conformance in a visual manner.

Identificador

http://eprints.qut.edu.au/61561/

Publicador

IEEE

Relação

DOI:10.1109/TKDE.2013.130

Vanden Broucke, S.K.L.M, De Weerdt, J., Vanthienen , J., & Baesens, B. (2014) Determining process model precision and generalization with weighted artificial negative events. IEEE Transactions on Knowledge and Data Engineering, 26(8), pp. 1877-1889.

http://purl.org/au-research/grants/ARC/DP120101624

Fonte

Information Systems; Science & Engineering Faculty

Palavras-Chave #080600 INFORMATION SYSTEMS #process mining #conformance checking #artificial negative events #precision #generalization
Tipo

Journal Article