Improving Workflow Anomaly Detection with a C-Tree

Feng-Jian Wang, Alex Chang, Tennyson Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

To guarantee the correctness of workflow execution, it is essential to analyze the structural and artifact integrity of workflows. The current best approach of artifact workflow anomaly detection is O(|E|) for structured workflows, however, each of the anomalies returned in the approach contains (artifact, operator) at each workflow node. In this paper, we present an innovative methodology which contains the following two characteristics: 1) A C-Tree (defined in Section 3) structure which separates sequential and parallel issues in workflow analysis and increases the convenience and elegancy of anomaly detection; and 2) A loop-reduction method which helps lower the size of nodes to be analyzed while not losing the abilities of detecting anomalies within workflow models. The anomaly detection is done by 1) transforming the BPMN into the C-Tree, 2) and detecting the anomaly in the C-tree. Compared with current best approach, 1) Our method can show the first operator and its location of an anomaly detected directly, although it cannot speed up the execution time, 2) The execution times of anomaly detection inside loop is decreased, and 3) Our method can detect concurrent (parallel) workflow anomaly based on C-Tree.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 41st Annual Computer Software and Applications Conference Workshops, COMPSAC 2017
EditorsClaudio Demartini, Ji-Jiang Yang, Sheikh Iqbal Ahamed, Thomas Conte, Toyokazu Akiyama, Sorel Reisman, Hiroki Takakura, Kamrul Hasan, William Claycomb, Motonori Nakamura, Edmundo Tovar, Zhiyong Zhang, Ling Liu, Chung-Horng Lung, Stelvio Cimato
PublisherIEEE Computer Society
Pages437-444
Number of pages8
ISBN (Electronic)9781538603673
DOIs
StatePublished - 7 Sep 2017
Event41st IEEE Annual Computer Software and Applications Conference Workshops, COMPSAC 2017 - Torino, Italy
Duration: 4 Jul 20178 Jul 2017

Publication series

NameProceedings - International Computer Software and Applications Conference
Volume2
ISSN (Print)0730-3157

Conference

Conference41st IEEE Annual Computer Software and Applications Conference Workshops, COMPSAC 2017
CountryItaly
CityTorino
Period4/07/178/07/17

Keywords

  • Artifact anomaly detection
  • Structured workflow

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  • Cite this

    Wang, F-J., Chang, A., & Lu, T. (2017). Improving Workflow Anomaly Detection with a C-Tree. In C. Demartini, J-J. Yang, S. I. Ahamed, T. Conte, T. Akiyama, S. Reisman, H. Takakura, K. Hasan, W. Claycomb, M. Nakamura, E. Tovar, Z. Zhang, L. Liu, C-H. Lung, & S. Cimato (Eds.), Proceedings - 2017 IEEE 41st Annual Computer Software and Applications Conference Workshops, COMPSAC 2017 (pp. 437-444). [8029970] (Proceedings - International Computer Software and Applications Conference; Vol. 2). IEEE Computer Society. https://doi.org/10.1109/COMPSAC.2017.277