Knowledge-based Data Access in Grid Environment.
Modern eScience approach requires usage of many advanced computer science technologies. The number of cooperating scientific teams constantly grows. Software utilities for storing and sharing the gathered knowledge in a global manner are necessary for a successful world-wide cooperation. Scientific experiments produce huge amounts of data. Relevant methods and software are necessary to access, integrate and process these data in any distributed computational environment.
Proper and efficient management of data storage systems, and especially HSM systems, is essential for many processes occurring in distributed computational environments such as: replica selection, new replica creation, specifying SLA parameters, guarantying the required quality of service or required level of data protection and availability. Currently, the computational environments, are often supplied with methods enabling to use knowledge represented by ontological descriptions of system components. In this way it is possible to use the distributed resources more efficiently and to create and integrate new applications easier using existing services semantically described.
The aim of the project is to work out a methodology concerning organization of data access in knowledge supported distributed computational environment with respect to various types of storage systems. Since the semantic approach offers more flexible and more general representation of data and resources our solution is based on applying semantic technologies, using ontologies, to model storage systems and to organize data access. As part of this ongoing research a Common Mass Storage System Model (CMSSM) for describing the state of any storage system including HSM systems is being developed. Relevant CMSSM ontology and corresponding services are being developed too.
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