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dc.contributor.authorMahmoud, MS
dc.contributor.authorEnsor, A
dc.contributor.authorBiem, A
dc.contributor.authorElmegreen, B
dc.contributor.authorGulyaev, S
dc.date.accessioned2013-06-05T20:53:21Z
dc.date.accessioned2013-06-05T22:55:01Z
dc.date.available2013-06-05T20:53:21Z
dc.date.available2013-06-05T22:55:01Z
dc.date.copyright2013
dc.date.issued2013-06-06
dc.identifier.citationStudies in Computational Intelligence, vol.426, pp.129 - 156
dc.identifier.issn1860-949X
dc.identifier.urihttp://hdl.handle.net/10292/5410
dc.description.abstractNew approaches for data provenance and data management (DPDM) are required for mega science projects like the Square Kilometer Array, characterized by extremely large data volume and intense data rates, therefore demanding innovative and highly efficient computational paradigms. In this context, we explore a stream-computing approach with the emphasis on the use of accelerators. In particular, we make use of a new generation of high performance stream-based parallelization middleware known as InfoSphere Streams. Its viability for managing and ensuring interoperability and integrity of signal processing data pipelines is demonstrated in radio astronomy. IBM InfoSphere Streams embraces the stream-computing paradigm. It is a shift from conventional data mining techniques (involving analysis of existing data from databases) towards real-time analytic processing. We discuss using InfoSphere Streams for effective DPDM in radio astronomy and propose a way in which InfoSphere Streams can be utilized for large antennae arrays. We present a case-study: the InfoSphere Streams implementation of an autocorrelating spectrometer, and using this example we discuss the advantages of the stream-computing approach and the utilization of hardware accelerators.
dc.publisherSpringer
dc.relation.replaceshttp://hdl.handle.net/10292/5409
dc.relation.replaces10292/5409
dc.rightsAn author may self-archive an author-created version of his/her article on his/her own website and or in his/her institutional repository. He/she may also deposit this version on his/her funder’s or funder’s designated repository at the funder’s request or as a result of a legal obligation, provided it is not made publicly available until 12 months after official publication. He/ she may not use the publisher's PDF version, which is posted on www.springerlink.com, for the purpose of self-archiving or deposit. Furthermore, the author may only post his/her version provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at www.springerlink.com”. (Please also see Publisher’s Version and Citation).
dc.titleData provenance and management in radio astronomy: a stream computing approach
dc.typeJournal Article
dc.rights.accessrightsOpenAccess
dc.identifier.doi10.1007/978-3-642-29931-5_6
aut.relation.endpage156
aut.relation.startpage129
aut.relation.volume426
pubs.elements-id130790


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