Processing and managing scientific data in SOA environment

Increased complexity of scientific research poses new challenges to scientific data management. Meanwhile, scientific collaboration is becoming increasing important, which relies on integrating and sharing data from distributed institutions. Scientific experiments require effective and efficient management of data. In this paper we present an integrated, extensible architecture based on service-oriented approach that addresses large collections of heterogeneous scientific data. This architecture provides capabilities to scientists to model their experiments, enabling complex interconnections between computational simulations, data transformations applications, and analysis and visualisation tools. Key-Words: service-oriented architecture (SOA), scientific data, meta data, data management.

[1]  Edward D. Lazowska,et al.  Trident: Scientific Workflow Workbench for Oceanography , 2008, 2008 IEEE Congress on Services - Part I.

[2]  Rolf Werner,et al.  The latitudinal ozone variability study using wavelet analysis , 2008 .

[3]  J. Robert Keeley,et al.  Modeling generic oceanographic data objects in XML , 2005, Computing in Science & Engineering.

[4]  Evangelos Floros,et al.  ServOSims: A Service Oriented Framework for Composing and Executing Multidisciplinary Simulations , 2006, 2006 Second IEEE International Conference on e-Science and Grid Computing (e-Science'06).

[5]  Robert Gruber,et al.  PADS: a domain-specific language for processing ad hoc data , 2005, PLDI '05.

[6]  Jane Hunter,et al.  Semi-automated preservation and archival of scientific data using semantic grid services , 2005, CCGrid 2005. IEEE International Symposium on Cluster Computing and the Grid, 2005..

[7]  Karen Schuchardt,et al.  Iterative Workflows for Numerical Simulations in Subsurface Sciences , 2008, 2008 IEEE Congress on Services - Part I.

[8]  Chuang Li,et al.  FSML: Fusion Simulation Markup Language for interoperability of data and analysis tools , 2005, CLADE 2005. Proceedings Challenges of Large Applications in Distributed Environments, 2005..

[9]  Kumwon Cho,et al.  X-SIGMA: XML Based Simple Data Integration System for Gathering, Managing, and Accessing Scientific Experimental Data in Grid Environments , 2006, 2006 Second IEEE International Conference on e-Science and Grid Computing (e-Science'06).

[10]  L. Ian Lumb,et al.  Grid-enabling the Global Geodynamics Project: the introduction of an XML-based data model , 2005, 19th International Symposium on High Performance Computing Systems and Applications (HPCS'05).