Journal of Grid Computing

, Volume 11, Issue 3, pp 505–522 | Cite as

Exploring Workflow Interoperability for Neuroimage Analysis on the SHIWA Platform

  • Vladimir KorkhovEmail author
  • Dagmar Krefting
  • Tamas Kukla
  • Gabor Z. Terstyanszky
  • Matthan W. A. Caan
  • Silvia D. Olabarriaga


Neuroimaging is a field that benefits from distributed computing infrastructures (DCIs) to perform data processing and analysis, which is often achieved using Grid workflow systems. Collaborative research in neuroimaging requires ways to facilitate exchange between different groups, in particular to enable sharing, re-use and interoperability of applications implemented as workflows. The SHIWA project provides solutions to facilitate sharing and exchange of workflows between workflow systems and DCI resources. In this paper we present and analyse how the SHIWA Platform was used to implement various cases in which workflow exchange supports collaboration in neuroscience. The SHIWA Platform and the implemented solutions are described and analysed from a “user” perspective, in this case workflow developers and neuroscientists. We conclude that the platform in its current form is valuable for these cases, and we identify remaining challenges.


Neuroimage analysis Grid Interoperability Workflow 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Belloum, A., Inda, M.A., Vasunin, D., Korkhov, V., Zhao, Z., H.Rauwerda, Breit, T.M., Bubak, M., Hertzberger, L.O.: Collaborative e-Science Experiments and Scientific Workflows. IEEE Internet Comput. 15(4), 39–47 (2011)CrossRefGoogle Scholar
  2. 2.
    Baduel, L., Baude, F., Caromel, D., Contes, A., Huet, F., Morel, M., Quilici, R.: Grid Computing: Software Environments and Tools, chapter Programming, Deploying, Composing, for the Grid. Springer-Verlag (2006)Google Scholar
  3. 3.
    Barga, R., Gannon, D.: Scientific versus business workflows. In: Taylor, I. et al. (eds.) Workflows for e-Science, pp. 9–18. Springer London (2007)Google Scholar
  4. 4.
    Basney, J., Humphrey, M., Welch, V.: The MyProxy online credential repository. Softw. Pract. Ex. 35(9), 801–816 (2005)CrossRefGoogle Scholar
  5. 5.
    Behrens, T.E.J., Johansen, H., Berg, Jbabdi, S., Rushworth, M.F.S., Woolrich, M.W.: Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? Neuroimage 34(1), 144–155 (2007)CrossRefGoogle Scholar
  6. 6.
    Deelman, E., Singh, G., hui Su, M., Blythe, J., Gil, Y., Kesselman, C., Mehta, G., Vahi, K., Berriman, G.B., Good, J., Laity, A., Jacob, J.C., Katz, D.S.: Pegasus: a framework for mapping complex scientific workflows onto distributed systems. Sci. Program. J. 13, 219–237 (2005)Google Scholar
  7. 7.
    Delaitre, T., Kiss, T., Goyeneche, A., Terstyanszky, G., Winter, S., and Kacsuk, P.: Gemlca: running legacy code applications as Grid services. J. Grid Comput. 3(1–2), 75–90 (2005)CrossRefGoogle Scholar
  8. 8.
    Riedel, M. (ed.): International Grid Interoperability and Interoperation Workshop. IEEE: Indianapolis, USA (2008)Google Scholar
  9. 9.
    Elmroth, E., Hernández, F., Tordsson, J.: Three fundamental dimensions of scientific workflow interoperability: model of computation, language, and execution environment. Future Gener. Comput. Syst. 26(2), 245–256 (2010)CrossRefGoogle Scholar
  10. 10.
    Redolfi, A., et al.: Grid infrastructures for computational neuroscience: the neuGRID example. Future Neurol. 4(6), 703–722 (2009)CrossRefGoogle Scholar
  11. 11.
    Fernando, S.D.I., Creager, D.A., Simpson, A.C.: Towards build-time interoperability of workflow definition languages. In: Negru, V. et al. (eds.) SYNASC 2007, 9th International Symposium on Symbolic and Numberic Algorithms for Scientific Computing, pp. 525–532 (2007)Google Scholar
  12. 12.
    Glatard, T., Montagnat, J., Lingrand, D., Pennec, X.: Flexible and efficient workflow deployement of data-intensive applications on Grids with MOTEUR. Int. J. High Perform. Comput. Appl. 22(3), 347–360 (2008)CrossRefGoogle Scholar
  13. 13.
    Goecks, J., Nekrutenko, A., Taylor, J., The Galaxy Team: Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol. 11(8), R86 (2010)CrossRefGoogle Scholar
  14. 14.
    Hoheisel, A.: Grid workflow execution service – dynamic and interactive execution and visualization of distributed workflows. In: Proceedings of the Cracow Grid Workshop 2006, Academic Computer Center CYFRONET AGH, vol. II, pp. 13–24 (2007)Google Scholar
  15. 15.
    Jordan, D., Evdemon, J. (chairs): Web Services Business Process Execution Language version 2.0. (2013)
  16. 16.
    Plankensteiner, K., Montagnat, J., Prodan, R.: IWIR: a language enabling portability across Grid workflow systems. In: Proceedings of 6th Workshop on Workflows in Support of Large-Scale Science (WORKS’11) as a part of Supercomputing’11 Conference. Seattle, USA (2011). doi: 10.1145/2110497.2110509
  17. 17.
    Kacsuk, P., Sipos, G.: Multi-Grid, multi-user workflows in the P-GRADE Grid portal. J Grid Comput. 3, 221–238 (2005)CrossRefGoogle Scholar
  18. 18.
    Krefting, D. et al.: MediGRID: towards a user friendly secured Grid infrastructure. Future Gener. Comput. Syst. 25, 326–336 (2009)CrossRefGoogle Scholar
  19. 19.
    Krefting, D., Glatard, T., Korkhov, V., Montagnat, J., Olabarriaga, S.: Enabling Grid interoperability at workflow level. In: Proceedings of Grid Workflow Workshop’11. Cologne, Germany (2011)Google Scholar
  20. 20.
    Krefting, D., Luetzkendorf, R., Peter, K., Bernarding, J.: Performance analysis of diffusion tensor imaging in an academic production Grid. In: Proceedings of 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp. 751–756. Melbourne, VIC, Australia IEEE Computer Society Conference Publishing Services (CPS) (2010)Google Scholar
  21. 21.
    Kukla, T., Kiss, T., Terstyanszky, G., Kacsuk, P.: A general and scalable solution for heterogeneous workflow invocation and nesting. In: Proceedings of 3rd Workshop on Workflows in Support of Large-Scale Science (WORKS’08), pp. 1–8 (2008)Google Scholar
  22. 22.
    Ludäscher, B., Altintas, I., Berkley, C., Higgins, D., Jaeger, E., Jones, M., Lee, E.A., Tao, J., Zhao, Y.: Scientific workflow management and the kepler system. Concurr. Comput. Pract. Ex. 18(10), 1039–1065 (2006)CrossRefGoogle Scholar
  23. 23.
    Luetzkendorf, R., Bernarding, J., Hertel, F., Viezens, F., Thiel, A., Krefting, D.: Enabling of Grid based diffusion tensor imaging using a workflow implementation of FSL. In: Proceedings of HealthGrid 2009, Studies in Health Technology and Informatics, vol. 147, pp. 72–81 (2009)Google Scholar
  24. 24.
    Oinn, T., Greenwood, M., Addis, M., et al.: Taverna: lessons in creating a workflow environment for the life sciences. J. Concur. Comput. Pract. Ex. 18(10), 1067–1100 (2006). Special Issue on Workflow in Grid SystemsCrossRefGoogle Scholar
  25. 25.
    Olabarriaga, S., Glatard, T., Hoheisel, A., Nederveen, A., Krefting, D.: Crossing healthGrid borders: early results in medical imaging. In: Proceedings of HealthGrid’09, Studies in Health Technology and Informatics, vol. 147, pp. 62–71. Berlin (2009)Google Scholar
  26. 26.
    Olabarriaga, S.D., Glatard, T., de Boer, P.T., A virtual laboratory for medical image analysis. IEEE Trans. Inf. Technol. Biomed. 14(4), 979–985 (2010)CrossRefGoogle Scholar
  27. 27.
    Klingenstein, K., Gannon, D., et al.: Improving interoperability, sustainability and platform convergence in scientific and scholarly workflow. NSF/Mellon Workshop on Scientific and Scholarly Workflow. (2007)
  28. 28.
    Rex, D., Ma, J., Toga, A.: The LONI pipeline processing environment. Neuroimage 19(3), 1033–1048 (2003)CrossRefGoogle Scholar
  29. 29.
    De Roure, D., Goble, C., Stevens, R.: The design and realisation of the myexperiment virtual research environment for social sharing of workflows. Future Gener. Comput. Syst. 25(5), 561–567 (2009)CrossRefGoogle Scholar
  30. 30.
    SHIWA Portal: (2013)
  31. 31.
    SHIWA project: (2013)
  32. 32.
    SHIWA Repository: (2013)
  33. 33.
  34. 34.
    Smith, S.M., et al.: Advances in functional and structural mr image analysis and implementation as fsl. Neuroimage 23(S1), 208–219 (2004)CrossRefGoogle Scholar
  35. 35.
    Fahringer, T., Jugravu, A., Pllana, S., Prodan, R., Seragiotto, Jr., C., Truong, H.-L.: ASKALON: a tool set for cluster and Grid computing. Concurr. Comput. Pract. Ex. 17(2–4), 143–169 (2005)CrossRefGoogle Scholar
  36. 36.
    Taylor, I., Shields, M., Wang, I., Harrison, A.: The triana workflow environment: architecture and applications. In: Taylor, I., Deelman, E., Gannon, D., Shields, M. (eds.) Workflows for e-Science, pp. 320–339. Springer, New York, Secaucus, NJ, USA (2007)Google Scholar
  37. 37.
    Korkhov, V., Krefting, D., Kukla, T., Terstyanszky, G., Caan, M., Olabarriaga, S.: Exploring workflow interoperability tools for neuroimaging data analysis. In: Proceedings of 6th Workshop on Workflows in Support of Large-Scale Science (WORKS’11) as a part of Supercomputing’11 Conference, pp. 87–96. Seattle, USA (2011). doi: 10.1145/2110497.2110508
  38. 38.
    Korkhov, V., Vasyunin, D., Wibisono, A., Guevara-Masis, V., Belloum, A., de Laat, C., Adriaans, P., Hertzberger, L.O.: WS-VLAM: towards a scalable workflow system on the Grid. In: Proceedings of the 2nd workshop on Workflows in Support of Large-Scale Science (WORKS07), 16th IEEE International Symposium on High Performance Distributed Computing, pp. 63–68 (2007)Google Scholar
  39. 39.
    Wibisono, A., Vasyunin, D., Korkhov, V., Zhao, Z., Belloum, A., Laat, C., Adriaans, P., Hertzberger, B.: WS-VLAM: A GT4 based workflow management system. In: Shi, Y., van Albada, D., Dongarra, J., Sloot, P.M.A. (eds.) Computational Science ICCS 2007. Lecture Notes in Computer Science, vol. 4489, pp. 191–198. Springer Berlin Heidelberg (2007)Google Scholar
  40. 40.
    Zhao, Z., Belloum, A., de Laat, C., Adriaans, P., Hertzberger, B.: Using Jade agent framework to prototype an e-Science workflow bus. In: CCGrid, Rio de Janeiro, Brazil, pp. 655–660. IEEE (2007)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Vladimir Korkhov
    • 1
    • 2
    Email author
  • Dagmar Krefting
    • 3
  • Tamas Kukla
    • 4
  • Gabor Z. Terstyanszky
    • 4
  • Matthan W. A. Caan
    • 2
  • Silvia D. Olabarriaga
    • 2
  1. 1.Faculty of Applied Mathematics and Control ProcessesSt. Petersburg State UniversitySt. PetersburgRussia
  2. 2.Academic Medical CenterUniversity of AmsterdamAmsterdamThe Netherlands
  3. 3.University of Applied Sciences BerlinBerlinGermany
  4. 4.Centre for Parallel ComputingUniversity of WestminsterLondonUK

Personalised recommendations