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Semantic Similarity of Workflow Traces with Various Granularities

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Web Information Systems Engineering – WISE 2016 (WISE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10041))

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Abstract

A workflow trace describes provenance information of a particular workflow execution. Understanding workflow traces and their similarity have many applications in both scientific research and business world. Given workflow traces generated by heterogeneous systems with difference granularities, it is a challenge for users to understand their similarities. In this work, we investigate workflow traces’ granularity problem and their similarity method. Algorithms are developed to transform a trace into its multi-granularity forms assisting by a workflow trace ontology. A novel generic semantic similarity algorithm is proposed that not only considers the structural similarity but also the semantics coverage embedded in traces during transformation. Furthermore, theoretical analysis is presented to compute the maximum semantic similarity. Our approach enables that two workflow traces can be compared with any granularity. The experiment using real world workflow traces demonstrates the effectiveness of the proposed methods.

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Notes

  1. 1.

    https://confluence.pegasus.isi.edu/display/pegasus/WorkflowGenerator.

  2. 2.

    https://confluence.pegasus.isi.edu/display/pegasus/WorkflowGenerator.

References

  1. Bao, Z., Cohen-Boulakia, S., Davidson, S.B., Eyal, A., Khanna, S.: Differencing provenance in scientific workflows. In: ICDE, pp. 808–819 (2009)

    Google Scholar 

  2. Bowers, S.: Scientific workflow, provenance, and data modeling challenges and approaches. J. Data Semant. 1(1), 19–30 (2012)

    Article  Google Scholar 

  3. Chapman, A.P., Jagadish, H.V., Ramanan, P.: Efficient provenance storage. In: SIGMOD, pp. 993–1006 (2008)

    Google Scholar 

  4. Gotz, D., Zhou, M.X.: Characterizing users’ visual analytic activity for insight provenance. Inf. Vis. 8(1), 42–55 (2009)

    Article  Google Scholar 

  5. Groth, P., Moreau, L.: Prov overview. W3C Working Draft, 11 December 2012

    Google Scholar 

  6. Liu, Q., Zhao, X., Taylor, K., Lin, X., Squire, G., Kloppers, C., Miller, R.: Towards semantic comparison of multi-granularity process traces. Knowl. Based Syst. 52, 91–106 (2013)

    Article  Google Scholar 

  7. David Allen, M., Len Seligman, B.: Provenance capture and use: a practical guide. MITRE Corporation (2010)

    Google Scholar 

  8. Moreau, L., Clifford, B., Freire, J., Futrelle, J., Gil, Y., Groth, P., Kwasnikowska, N., Miles, S., Missier, P., Myers, J., et al.: The open provenance model core specification (v1. 1). Future Gener. Comput. Syst. 27(6), 743–756 (2011)

    Article  Google Scholar 

  9. Scheidegger, C., Koop, D., Santos, E., Vo, H., Callahan, S., Freire, J., Silva, C.: Tackling the provenance challenge one layer at a time. Concurrency Comput. Pract. Exp. 20(5), 473–483 (2008)

    Article  Google Scholar 

  10. Stephan, E.G., Halter, T.D., Ermold, B.D.: Leveraging the open provenance model as a multi-tier model for global climate research. In: McGuinness, D.L., Michaelis, J.R., Moreau, L. (eds.) IPAW 2010. LNCS, vol. 6378, pp. 34–41. Springer, Heidelberg (2010). doi:10.1007/978-3-642-17819-1_5

    Chapter  Google Scholar 

  11. Xie, Y., Muniswamy-Reddy, K.K., Long, D.D., Amer, A., Feng, D., Tan, Z.: Compressing provenance graphs. In: Tapp (2011)

    Google Scholar 

  12. Zhao, J., Wroe, C., Goble, C., Stevens, R., Quan, D., Greenwood, M.: Using semantic web technologies for representing e-Science provenance. In: McIlraith, S.A., Plexousakis, D., Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 92–106. Springer, Heidelberg (2004). doi:10.1007/978-3-540-30475-3_8

    Chapter  Google Scholar 

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Correspondence to Qing Liu .

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Liu, Q., Bai, Q., Yang, Y. (2016). Semantic Similarity of Workflow Traces with Various Granularities. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10041. Springer, Cham. https://doi.org/10.1007/978-3-319-48740-3_15

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  • DOI: https://doi.org/10.1007/978-3-319-48740-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48739-7

  • Online ISBN: 978-3-319-48740-3

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