A temporal data-mining approach for discovering end-to-end transaction flows

Chang Shing Perng, Tao Tao, Chungqiang Tang, Edward So, Chun Zhang, Rong Chang, Ting Wang, Ling Liu

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

1 Citation (Scopus)

Abstract

Effective management of Web Services systems relies on accurate understanding of end-to-end transaction flows, which may change over time as the service composition evolves. This work takes a data mining approach to automatically recovering end-to-end transaction flows from (potentially obscure) monitoring events produced by monitoring tools. We classify the caller-callee relationships among monitoring events into three categories (identity, direct-invoke, and cascaded-invoke), and propose unsupervised learning algorithms to generate rules for each type of relationship. The key idea is to leverage the temporal information available in the monitoring data and extract patterns that have statistical significance. By piecing together the caller-callee relationships at each step along the invocation path, we can recover the end-to-end flow for every executed transaction. Experiments demonstrate that our algorithms outperform human experts in terms of solution quality, scale well with the data size, and are robust against noises in monitoring data.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE International Conference on Web Services, ICWS 2008
Pages37-44
Number of pages8
DOIs
StatePublished - Dec 24 2008
EventIEEE International Conference on Web Services, ICWS 2008 - Beijing, China
Duration: Sep 23 2008Sep 26 2008

Publication series

NameProceedings of the IEEE International Conference on Web Services, ICWS 2008

Conference

ConferenceIEEE International Conference on Web Services, ICWS 2008
CountryChina
CityBeijing
Period9/23/089/26/08

Fingerprint

Data mining
Monitoring
Unsupervised learning
Web services
Learning algorithms
Chemical analysis
Experiments

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Software
  • Electrical and Electronic Engineering

Cite this

Perng, C. S., Tao, T., Tang, C., So, E., Zhang, C., Chang, R., ... Liu, L. (2008). A temporal data-mining approach for discovering end-to-end transaction flows. In Proceedings of the IEEE International Conference on Web Services, ICWS 2008 (pp. 37-44). [4670157] (Proceedings of the IEEE International Conference on Web Services, ICWS 2008). https://doi.org/10.1109/ICWS.2008.59
Perng, Chang Shing ; Tao, Tao ; Tang, Chungqiang ; So, Edward ; Zhang, Chun ; Chang, Rong ; Wang, Ting ; Liu, Ling. / A temporal data-mining approach for discovering end-to-end transaction flows. Proceedings of the IEEE International Conference on Web Services, ICWS 2008. 2008. pp. 37-44 (Proceedings of the IEEE International Conference on Web Services, ICWS 2008).
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Perng, CS, Tao, T, Tang, C, So, E, Zhang, C, Chang, R, Wang, T & Liu, L 2008, A temporal data-mining approach for discovering end-to-end transaction flows. in Proceedings of the IEEE International Conference on Web Services, ICWS 2008., 4670157, Proceedings of the IEEE International Conference on Web Services, ICWS 2008, pp. 37-44, IEEE International Conference on Web Services, ICWS 2008, Beijing, China, 9/23/08. https://doi.org/10.1109/ICWS.2008.59

A temporal data-mining approach for discovering end-to-end transaction flows. / Perng, Chang Shing; Tao, Tao; Tang, Chungqiang; So, Edward; Zhang, Chun; Chang, Rong; Wang, Ting; Liu, Ling.

Proceedings of the IEEE International Conference on Web Services, ICWS 2008. 2008. p. 37-44 4670157 (Proceedings of the IEEE International Conference on Web Services, ICWS 2008).

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

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Perng CS, Tao T, Tang C, So E, Zhang C, Chang R et al. A temporal data-mining approach for discovering end-to-end transaction flows. In Proceedings of the IEEE International Conference on Web Services, ICWS 2008. 2008. p. 37-44. 4670157. (Proceedings of the IEEE International Conference on Web Services, ICWS 2008). https://doi.org/10.1109/ICWS.2008.59