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International Conference on Web-Age Information Management

WAIM 2016: Web-Age Information Management pp 65–77Cite as

Detecting Data-model-oriented Anomalies in Parallel Business Process

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9659))

Abstract

Currently, most information systems are data intensive. The data models of such are posing notable influence on business processes. However, the predominance of existed process verification methods leave out the impact of data models on process models. Meanwhile, with parallel structures in business processes multiplying, business process structures are becoming increasingly intricate and large in size. A parallel structure engenders also uncertainty, and consequently increases the chances and decreases the detectability of anomalies occasioned by process and data model conflicts. In this paper, these anomalies are analyzed and classified. A data state matrix and data operation algebra is introduced to establish the relation between the parallel-process model and the data model. Then, an anomaly detection method under the divide-and-conquer framework is proposed to ensure efficiency in detecting anomalies in business processes. Both theoretical analysis and experimental results prove this method to be highly efficient and effective in detecting data model oriented anomalies.

This work was supported by Natural Science Foundation of China (No. 61170003).

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Author information

Authors and Affiliations

  1. Key Laboratory of Machine Perception, Ministry of Education, Peking University, Beijing, 100871, China

    Ning Yin, Shanshan Wang, Hongyan Li & Lilue Fan

  2. School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Ning Yin, Shanshan Wang & Hongyan Li

Authors
  1. Ning Yin
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  2. Shanshan Wang
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  3. Hongyan Li
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  4. Lilue Fan
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Corresponding author

Correspondence to Hongyan Li .

Editor information

Editors and Affiliations

  1. Peking University , Beijing, China

    Bin Cui

  2. The George Washington University , Washington, D.C., USA

    Nan Zhang

  3. Hong Kong Baptist University, Kowloon Tong, Hong Kong, China

    Jianliang Xu

  4. University of Texas Rio Grande Valley, Edinburg, Texas, USA

    Xiang Lian

  5. Jiangxi University of Finance and Economics, Nanchang, Jiangxi, China

    Dexi Liu

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© 2016 Springer International Publishing Switzerland

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Yin, N., Wang, S., Li, H., Fan, L. (2016). Detecting Data-model-oriented Anomalies in Parallel Business Process. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9659. Springer, Cham. https://doi.org/10.1007/978-3-319-39958-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-39958-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39957-7

  • Online ISBN: 978-3-319-39958-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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