Advertisement

Evaluating Workflow Trust Using Hidden Markov Modeling and Provenance Data

  • Mahsa Naseri
  • Simone A. Ludwig
Part of the Studies in Computational Intelligence book series (SCI, volume 426)

Abstract

In service-oriented environments, services with different functionalities are combined in a specific order to provide higher-level functionality. Keeping track of the composition process along with the data transformations and services provides a rich amount of information for later reasoning. This information, which is referred to as provenance, is of great importance and has found its way into areas of computer science such as bioinformatics, database, social, sensor networks, etc. Current exploitation and application of provenance data is limited as provenance systems have been developed mainly for specific applications. Therefore, there is a need for a multi-functional architecture, which is application-independent and can be deployed in any area. In this chapter we describe the multi-functional architecture as well as one component, which we call workflow evaluation. Assessing the trust value of a workflow helps to determine its rate of reliability. Therefore, the trustworthiness of the results of a workflow will be inferred to decide whether the workflow’s trust rate should be improved. The improvement can be done by replacing services with low trust levels with services with higher trust levels. We provide a new approach for evaluating workflow trust based on the Hidden Markov Model (HMM). We first present how the workflow trust evaluation can be modeled as a HMM and provide information on how the model and its associated probabilities can be assessed. Then, we investigate the behavior of our model by relaxing the stationary assumption of HMM and present another model based on non-stationary hidden Markov models. We compare the results of the two models and present our conclusions.

Keywords

Hide Markov Model Hide State Service Selection Trust Evaluation Trust Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brown, B., Aaron, M.: The politics of nature. In: Smith, J. (ed.) The Rise of Modern Genomics, 3rd edn. Wiley, New York (2001)Google Scholar
  2. 2.
    Naseri, M., Ludwig, S.A.: A Multi-Functional Architecture Addressing Workflow and Service Challenges Using Provenance Data. In: Proceedings of Workshop for Ph.D. Students in Information and Knowledge Management (PIKM) in Conjunction with the 19th ACM Conference on Information and Knowledge Management (CIKM), Toronto, Canada (2010)Google Scholar
  3. 3.
    Gaaloul, W., Baïna, K., Godart, C.: Towards Mining Structural Workflow Patterns. In: Andersen, K.V., Debenham, J., Wagner, R. (eds.) DEXA 2005. LNCS, vol. 3588, pp. 24–33. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Altintas, A.: Lifecycle of Scientific Workflows and Their Provenance: A Usage Perspective. In: Proceeding of 2008 IEEE Congress on Services (2008)Google Scholar
  5. 5.
    Kim, J., et al.: Provenance trails in the Wings-Pegasus system. Concurrency and Computation: Practice and Experience 20 (2007)Google Scholar
  6. 6.
    Aiello, R.: Workflow Performance Evaluation. PhD Thesis, University of Salerno, Italy (2004)Google Scholar
  7. 7.
    Gil, Y.: Workflow Composition: Semantic Representations for Flexible Automation. In: Workflows for e-Science, pp. 244–257 (2007)Google Scholar
  8. 8.
    Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, 257–286 (1989)Google Scholar
  9. 9.
    Krogh, A., Mian, S.I., Haussler, D.: A Hidden Markov Model that finds genes in E. coli DNA. Nucleic Acids Research 22, 4768–4778 (1994)CrossRefGoogle Scholar
  10. 10.
    Jelinek, F.: Self-organized Language Modeling for Speech Recognition, IBM T.J. Watson Research Center Technical Report (1985)Google Scholar
  11. 11.
    Bongkee, S., Jin, H.K.: Nonstationary Hidden Markov Model. Signal Processing 46, 31–46 (1995)zbMATHCrossRefGoogle Scholar
  12. 12.
    JingHui, X., BingQuan, L., XiaLong, W.: Principles of Non-stationary Hidden Markov Model and its Applications to Sequence Labeling Task. In: Proceedings of the Second International Joint Conference on Natural Language Processing (2005)Google Scholar
  13. 13.
    Fine, S., Singer, Y., Tishby, N.: The Hierarchical Hidden Markov Model: Analysis and Applications. Machine Learning 32, 41–62 (1998)zbMATHCrossRefGoogle Scholar
  14. 14.
    Altintas, I.: Lifecycle of Scientific Workflows and their Provenance: A Usage Perspective. In: IEEE Congress on Services 2008- Part I (2008)Google Scholar
  15. 15.
    Verdonck, F., Jaworska, J., Thas, O., Vanrolleghem, P.: Determining Environmental Standards using Bootstrapping, Bayesian and Maximum Likelihood Techniques: A Comparative Study. Analytica Chimica Acta 446, 429–438 (2001)CrossRefGoogle Scholar
  16. 16.
    Fayyad, M., Piatetsky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery: An Overview. In: Advances in Knowledge Discovery and Data Mining, pp. 1–34. AAAI Press / The MIT Press, Menlo Park (1996)Google Scholar
  17. 17.
    Forney, G.D.: The Viterbi Algorithm. Proceedings of the IEEE 61(3) (1973)Google Scholar
  18. 18.
    MySQL DataBase Software, www.mysql.com
  19. 19.
    Rajbhandari, S., Wootten, I., Shaikh Ali, A., Rana, O.F.: Evaluating Provenance-based Trust for Scientific Workflows. In: Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid (2006)Google Scholar
  20. 20.
    Rajbhandari, S., Rana, O.F., Wootten, I.: A Fuzzy Model for Calculating Workflow Trust using Provenance Data. In: Proceedings of the 15th ACM Mardi Gras Conference (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of SaskatchewanSaskatoonCanada
  2. 2.Department of Computer ScienceNorth Dakota State UniversityFargoUSA

Personalised recommendations