Data Provenance and Data Management in eScience

eScience allows scientific research to be carried out in highly distributed environments. The complex nature of the interactions in an eScience infrastructure, which often involves a range of instruments, data, models, applications, people and computational facilities, suggests there is a need for d...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Liu, Qing (Editor ), Bai, Quan (Editor ), Giugni, Stephen (Editor ), Williamson, Darrell (Editor ), Taylor, John (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
Edición:1st ed. 2013.
Colección:Studies in Computational Intelligence, 426
Temas:
Engineering.
Artificial intelligence.
Technology and Engineering.
Artificial Intelligence.
Acceso en línea:Texto Completo
Descripción
Sumario:eScience allows scientific research to be carried out in highly distributed environments. The complex nature of the interactions in an eScience infrastructure, which often involves a range of instruments, data, models, applications, people and computational facilities, suggests there is a need for data provenance and data management (DPDM). The W3C Provenance Working Group defines the provenance of a resource as a "record that describes entities and processes involved in producing and delivering or otherwise influencing that resource". It has been widely recognised that provenance is a critical issue to enable sharing, trust, authentication and reproducibility of eScience process.   Data Provenance and Data Management in eScience identifies the gaps between DPDM foundations and their practice within eScience domains including clinical trials, bioinformatics and radio astronomy. The book covers important aspects of fundamental research in DPDM including provenance representation and querying. It also explores topics that go beyond the fundamentals including applications. This book is a unique reference for DPDM with broad appeal to anyone interested in the practical issues of DPDM in eScience domains.
Descripción Física:XII, 184 p. online resource.
ISBN:9783642299315
ISSN:1860-9503 ;

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