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Mining Workflow Repositories for Improving Fragments Reuse

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Semantic Keyword-Based Search on Structured Data Sources (IKC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9398))

Abstract

Public repositories of scientific and business workflows are gaining growing attention as a means to enable understanding, reuse and ultimately the reproducibility of the processes such workflows incarnate. However, as the number of workflows hosted by such repositories grows, their users face difficulties when it come to exploring and querying workflows. In this paper, we explore a functionality that can help repository administrators to index their workflows, and users to identify the workflows that are of interest to them. In particular, we investigate the problem of finding frequent and similar fragments in workflows using graph mining techniques. Our objective is not to come up with yet another graph mining or similarity technique. Instead, we explore different representations that can be used for encoding workflows before assessing their similarity taking into consideration the effectiveness and efficiency of the mining algorithm.

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Notes

  1. 1.

    http://www.myexperiment.org/home.

References

  1. Roure, D.D., Goble, C.A., Stevens, R.: The design and realisation of the myExperiment virtual research environment for social sharing of workflows. Future Generation Comp. Syst. 25(5), 561–567 (2009)

    Article  Google Scholar 

  2. Mates, P., Santos, E., Freire, J., Silva, C.T.: CrowdLabs: social analysis and visualization for the sciences. In: Bayard Cushing, J., French, J., Bowers, S. (eds.) SSDBM 2011. LNCS, vol. 6809, pp. 555–564. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  3. Giardine, B., Riemer, C., Hardison, R.C., Burhans, R., Shah, P., Zhang, Y., Blankenberg, D., Albert, I., Miller, W., Kent, W.J., Nekrutenko, A.: Galaxy: a platform for interactive large-scale genome analysis. Genome Res 15, 1451–1455 (2005)

    Article  Google Scholar 

  4. Bae, J., Caverlee, J., Liu, L., Yan, H.: Process mining by measuring process block similarity. In: Eder, J., Dustdar, S. (eds.) BPM Workshops 2006. LNCS, vol. 4103, pp. 141–152. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  5. Goderis, A., Li, P., Goble, C.: Workflow discovery: the problem, a case study from e-science and a graph-based solution. In: International Conference on Web Services, ICWS 2006, pp. 313–19. IEEE, Chicago (2006)

    Google Scholar 

  6. Bergmann, R., Gil, Y.: Similarity assessment and efficient retrieval of semantic workflows. Inf. Syst. 40, 115–127 (2014)

    Article  Google Scholar 

  7. Burstein, M., Yaman, F., Oates, T.: A context driven approach for workflow mining. In: Proceedings of the 21st International Jont Conference on Artifical Intelligence, pp. 1798–1803. Morgan Kaufmann Publishers Inc., Pasadena (2009)

    Google Scholar 

  8. Leake, D.B., Kendall-Morwick, J.: Towards case-based support for e-science workflow generation by mining provenance. In: Althoff, K.-D., Bergmann, R., Minor, M., Hanft, A. (eds.) ECCBR 2008. LNCS (LNAI), vol. 5239, pp. 269–283. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  9. Diamantini, C., Potena, D., Storti, E.: Mining usage patterns from a repository of scientific workflows. In: Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, vol. 4, pp. 629–635. IEEE, Haikou (2007)

    Google Scholar 

  10. Diamantini, C., Potena, D., Storti, E.: Mining usage patterns from a repository of scientific workflows. In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 152–157. ACM, New York (2012)

    Google Scholar 

  11. Starlinger, J., Brancotte, B., Cohen-Boulakia, S., Leser, U.: Similarity search for scientific workflows. In: 40th International Conference on Very Large Data Bases, pp. 2150–8097. VLDB Endowment, Hangzhou (2014)

    Article  Google Scholar 

  12. Cuzzocrea, A., Diamantini, C., Genga, L., Potena, D., Storti, E.: A composite methodology for supporting collaboration pattern discovery via semantic enrichment and multidimensional analysis. In: 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR), pp. 459–464. IEEE, Tunis (2014)

    Google Scholar 

  13. Garijo, D., Corcho, Ó., Gil, Y.: Detecting common scientific workflow fragments using templates and execution provenance. In: Proceedings of the Seventh International Conference on Knowledge Capture, pp. 33–40. ACM, New York (2013)

    Google Scholar 

  14. Diamantini, C., Genga, L., Potena, D., Storti, E.: Innovation pattern analysis. In: 2013 International Conference on Collaboration Technologies and Systems (CTS), pp. 628–629. IEEE, San Diego (2013)

    Google Scholar 

  15. Garijo, D., Corcho, Ó., Gil, Y., Gutman, B.A., Dinov, I.D., Thompson, P.M., Toga, A.W.: Fragflow automated fragment detection in scientific workflows. In: 10th IEEE International Conference on e-Science, pp. 281–289. IEEE, Sao Paulo (2014)

    Google Scholar 

  16. Diamantini, C., Gengaand, L., Potena, D., Storti, E.: Discovering behavioural patterns in knowledge-intensive collaborative processes. In: Proceedings of the ECML/PKDD 2014 Workshop on New Frontiers in Mining Complex Patterns (NFmcp 2014) (2014)

    Google Scholar 

  17. Jonyer, I., Cook, D.J., Holder, L.B.: Graph-based hierarchical conceptual clustering. J. Mach. Learn. Res. 2, 19–43 (2001)

    MATH  Google Scholar 

  18. Spell, B.: Java api for wordnet searching (jaws). http://lyle.smu.edu/~tspell/jaws/index.html

  19. P. University: Princeton university “about wordnet” (2010). http://wordnet.princeton.edu/wordnet/

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Authors and Affiliations

  1. LAMSADE, Paris Dauphine University, Paris, France

    Mariem Harmassi, Daniela Grigori & Khalid Belhajjame

Authors
  1. Mariem Harmassi
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  2. Daniela Grigori
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  3. Khalid Belhajjame
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Corresponding author

Correspondence to Khalid Belhajjame .

Editor information

Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    Jorge Cardoso

  2. Huawei European Research Center, Munich, Germany

    Jorge Cardoso

  3. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Francesco Guerra

  4. Delft University of Technology, Delft, Zuid-Holland, The Netherlands

    Geert-Jan Houben

  5. University of Coimbra, Coimbra, Portugal

    Alexandre Miguel Pinto

  6. Università degli Studi di Trento, Trento, Italy

    Yannis Velegrakis

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Harmassi, M., Grigori, D., Belhajjame, K. (2015). Mining Workflow Repositories for Improving Fragments Reuse. In: Cardoso, J., Guerra, F., Houben, GJ., Pinto, A.M., Velegrakis, Y. (eds) Semantic Keyword-Based Search on Structured Data Sources. IKC 2015. Lecture Notes in Computer Science(), vol 9398. Springer, Cham. https://doi.org/10.1007/978-3-319-27932-9_7

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  • DOI: https://doi.org/10.1007/978-3-319-27932-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27931-2

  • Online ISBN: 978-3-319-27932-9

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