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Data Intensive Computing for Biodiversity [electronic resource] / by Sarinder K. Dhillon, Amandeep S. Sidhu.

Por: Colaborador(es): Tipo de material: TextoTextoSeries Studies in Computational Intelligence ; 485 | Studies in Computational Intelligence ; 485Editor: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Descripción: IX, 126 p. 69 illus. online resourceTipo de contenido:
  • text
Tipo de medio:
  • computer
Tipo de soporte:
  • online resource
ISBN:
  • 9783642380471
Trabajos contenidos:
  • SpringerLink (Online service)
Tema(s): Formatos físicos adicionales: Sin títuloClasificación CDD:
  • 006.3 23
Clasificación LoC:
  • Q342
Recursos en línea:
Contenidos:
Springer eBooksResumen: This book is focused on the development of a data integration framework for retrieval of biodiversity information from heterogeneous and distributed data sources. The data integration system proposed in this book links remote databases in a networked environment, supports heterogeneous databases and data formats, links databases hosted on multiple platforms, and provides data security for database owners by allowing them to keep and maintain their own data and to choose information to be shared and linked. The book is a useful guide for researchers, practitioners, and graduate-level students interested in learning state-of-the-art development for data integration in biodiversity.
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Introduction -- Preliminary Study -- Literature Review -- Methodology -- Biodiversity Databases -- Proposed Solution.

This book is focused on the development of a data integration framework for retrieval of biodiversity information from heterogeneous and distributed data sources. The data integration system proposed in this book links remote databases in a networked environment, supports heterogeneous databases and data formats, links databases hosted on multiple platforms, and provides data security for database owners by allowing them to keep and maintain their own data and to choose information to be shared and linked. The book is a useful guide for researchers, practitioners, and graduate-level students interested in learning state-of-the-art development for data integration in biodiversity.

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