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Data-intensive applications in e-Science require scalable solutions for storage as well as interactive tools for analysis of scientific data. It is important to be able to query the data in a storage-independent way, and to be able to obtain the results of the data-analysis incrementally (in contrast to traditional batch solutions). We use the RDF data model extended with multidimensional numeric arrays to represent the results, parameters, and other metadata describing scientific experiments, and SciSPARQL, an extension of the SPARQL language, to combine massive numeric array data and metadata in queries. To address the scalability problem we present an architecture that enables the same SciSPARQL queries to be executed on the RDF dataset whether it is stored in a relational DBMS or mapped over a specialized geographically distributed e-Science data store. In order to minimize access and communication costs, we represent the arrays with proxy objects, and retrieve their content lazily. We formulate typical analysis tasks from a computational biology application in terms of SciSPARQL queries, and compare the query processing performance with manually written scripts in MATLAB.