ABSTRACT
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 usage scenarios in which workflow exchange supports collaboration in neuroscience. The SHIWA platform and the implemented solutions are described and analysed from the "user" perspective, in this case the workflow developers and the neuroscientists. We conclude that the platform in its current form is valuable for the foreseen usage scenarios, and we identify remaining challenges concerning management of multiple credentials and data transfers across DCIs.
- R. Barga and D. Gannon. Scientific versus business workflows. In In I. Taylor et al., editors, Workflows for e-Science, pages 9--18, 2007.Google Scholar
- J. Basney, M. Humphrey, and V. Welch. The MyProxy Online Credential Repository. Software: Practice and Experience, 35(9):801--816, 2005. Google ScholarDigital Library
- T.E.J. Behrens, H. Johansen Berg, S. Jbabdi, M.F.S. Rushworth, and M.W. Woolrich. Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage, 34(1):144--155, 2007.Google ScholarCross Ref
- T. Delaitre, T. Kiss, A. Goyeneche, G. Terstyanszky, S.Winter, and P. Kacsuk. Gemlca: Running legacy code applications as grid services. Journal of Grid Computing, 3(1--2):75--90, 2005.Google ScholarCross Ref
- M. Riedel (editor). International Grid Interoperability and Interoperation Workshop, Indianapolis, USA. IEEE, December 2008.Google Scholar
- E. Elmroth, F. Hernández, and J. Tordsson. Three fundamental dimensions of scientific workflow interoperability: Model of computation, language, and execution environment. Future Generation Computer Systems, 26(2):245--256, 2010. Google ScholarDigital Library
- A. Redolfi et al. Grid infrastructures for computational neuroscience: the neuGRID example. Future Neurology, 4(6):703--722, 2009.Google ScholarCross Ref
- S.D.I. Fernando, D.A. Creager, and A.C. Simpson. Towards build-time interoperability of workflow definition languages. In In V. Negru et al., editors, SYNASC 2007, 9th international symposium on symbolic and numberic algorithms for scientific computing, pages 525--532, 2007. Google ScholarDigital Library
- T. Glatard, K. Boulebiar, and S. D. Olabarriaga. Workflow integration in VL-e medical. In Proceedings of the 21st IEEE International Symposium on Computer-Based Medical Systems, June 2008. Google ScholarDigital Library
- T. Glatard, J. Montagnat, D. Lingrand, and X. Pennec. Flexible and efficient workflow deployement of data-intensive applications on grids with MOTEUR. International Journal of High Performance Computing Applications, 22(3):347--360, 2008. Google ScholarDigital Library
- J. Goecks, A. Nekrutenko, J. Taylor, and The Galaxy Team. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biology, 11(8):R86, 2010.Google ScholarCross Ref
- A Hoheisel. Grid Workflow Execution Service--Dynamic and Interactive Execution and Visualization of Distributed Workflows. In Proceedings of the Cracow Grid Workshop, 2006.Google Scholar
- D. Jordan and J. Evdemon (chairs). Web Services Business Process Execution Language version 2.0. http://docs.oasis- open.org/wsbpel/2.0/wsbpel-v2.0.pdf.Google Scholar
- P. Kacsuk, K. Karoczkai, G. Hermann, G. Sipos, and J. Kovács. WS-PGRADE: Supporting parameter sweep applications in workflows. In Workflows in Support of Large-Scale Science, 2008. WORKS 2008. Third Workshop on, pages 1--10. IEEE, 2008.Google ScholarCross Ref
- P. Kacsuk and G. Sipos. Multi-Grid, Multi-User Workflows in the P-GRADE Grid Portal. Journal of Grid Computing, 3:221--238.Google ScholarCross Ref
- D. Krefting et al. MediGRID: Towards a user friendly secured grid infrastructure. Future Generation Computer Systems, 25:326--336, 2009. Google ScholarDigital Library
- D. Krefting, T. Glatard, V. Korkhov, J. Montagnat, and S. Olabarriaga. Enabling grid interoperability at workflow level. In Grid Workflow Workshop, 2011.Google Scholar
- D. Krefting, R. Luetzkendorf, K. Peter, and J. Bernarding. Performance Analysis of Diffusion Tensor Imaging in an Academic Production Grid. In 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, 2010. Google ScholarDigital Library
- T. Kukla, T. Kiss, G. Terstyanszky, and P. Kacsuk. A general and scalable solution for heterogeneous workflow invocation and nesting. pages 1--8, 2008.Google Scholar
- R. Luetzkendorf, J. Bernarding, F. Hertel, F. Viezens, A. Thiel, and D. Krefting. Enabling of Grid based Diffusion Tensor Imaging using a Workflow Implementation of FSL. In Healthgrid 2009.Google Scholar
- T. Oinn, M. Greenwood, and M. Addis et al. Taverna: Lessons in creating a workflow environment for the life sciences. Journal Concurrency and Computation: Practice & Experience - Special Issue on Workflow in Grid Systems, 18(10):1067--1100, 2006. Google ScholarDigital Library
- S. Olabarriaga, T. Glatard, A. Hoheisel, A. Nederveen, and D. Krefting. Crossing HealthGrid Borders: Early Results in Medical Imaging. In HealthGrid'09, Berlin, jun 2009.Google Scholar
- S. D. Olabarriaga, T. Glatard, and P. T. de Boer. A Virtual Laboratory for Medical Image Analysis. IEEE Transactions on Information Technology In Biomedicine, 14(4):979--985, 2010. Google ScholarDigital Library
- NSF/Mellon Workshop on Scientific and Scholarly Workflow. Improving interoperability, sustainability and platform convergence in scientific and scholarly workflow. https://spaces.internet2.edu/display/SciSchWorkflow/Home.Google Scholar
- D. Rex, J. Ma, and A. Toga. The LONI Pipeline Processing Environment. NeuroImage, 19(3):1033--1048, 2003.Google ScholarCross Ref
- D. De Roure, C. Goble, and R. Stevens. The Design and Realisation of the myExperiment Virtual Research Environment for Social Sharing of Workflows. Future Generation Computer Systems, 25(5):561--567, 2009. Google ScholarDigital Library
- SHIWA Portal. http://shiwa-portal.cpc.wmin.ac.uk/liferay-portal-6.0.5/.Google Scholar
- SHIWA project. http://www.shiwa-workflow.eu/.Google Scholar
- SHIWA Repository. http://www.shiwa-workflow.eu/wiki/-/wiki/Main/SHIWA+Repository.Google Scholar
- SHIWA Simulation Platform. http://www.shiwa-workflow.eu/wiki/-/wiki/Main/SHIWA+Simulation+Platform.Google Scholar
- Z. Zhao, A. Belloum, C. de Laat, P. Adriaans, and B. Hertzberger. Using Jade agent framework to prototype an e-Science workflow bus. In CCGrid, Rio de Janeiro, Brazil, pages 655--660. IEEE, May 2007. Google ScholarDigital Library
Index Terms
- Exploring workflow interoperability tools for neuroimaging data analysis
Recommendations
Exploring Workflow Interoperability for Neuroimage Analysis on the SHIWA Platform
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 ...
Solving the grid interoperability problem by P-GRADE portal at workflow level
Grid interoperability has recently become a major issue at Grid forums. Most of the current ideas try to solve the problem at the middleware level where unfortunately too many components (information system, broker, etc.) should be made interoperable. ...
P-GRADE portal family for grid infrastructures
P-GRADE portal is one of the most widely used general-purpose grid portal in Europe. The paper summarizes the most advanced features of P-GRADE, such as parameter sweep workflow execution, multi-grid workflow execution and integration with the DSpace ...
Comments