Discovering Causal Relations in Semantically-Annotated Probabilistic Business Process Diagrams

12 pages•Published: September 17, 2018

Abstract

Business Process Diagrams (BPDs) have been used for documenting, analyzing and optimizing business processes. Business Process Modeling and Notation (BPMN) provides a rich graphical notation which is supported by a formalization that permits users automating such tasks. Stochastic versions of BPMN allows designers to represent the probability every possible way a process can develop. Nevertheless, this support is not enough for representing conditional dependencies between events occurring during process development. We show how structural learning on a Bayesian Network obtained from a BPD is used for discovering causal relations between process events. Temporal precedence between events, captured in the BPD, is used for pruning and correcting the model discovered by an Inferred Causation (IC) algorithm. We illustrate our approach by detecting dishonest bidders in an on-line auction scenario.

Keyphrases: bayesian networks, business process diagrams, causality, constraints and uncertainty, description logics

In: Daniel Lee, Alexander Steen and Toby Walsh (editors). GCAI-2018. 4th Global Conference on Artificial Intelligence, vol 55, pages 29-40.

Links:	https://easychair.org/publications/paper/BMjX
	https://doi.org/10.29007/nd7r

BibTeX entry

@inproceedings{GCAI-2018:Discovering_Causal_Relations_Semantically,
  author    = {Hector Ceballos and Francisco Cantu},
  title     = {Discovering Causal Relations in Semantically-Annotated Probabilistic Business Process Diagrams},
  booktitle = {GCAI-2018. 4th Global Conference on Artificial Intelligence},
  editor    = {Daniel Lee and Alexander Steen and Toby Walsh},
  series    = {EPiC Series in Computing},
  volume    = {55},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/BMjX},
  doi       = {10.29007/nd7r},
  pages     = {29-40},
  year      = {2018}}

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