Rishi sinha (6 risultati)

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  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing, 2010

    3838310411 / 9783838310411

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    Da: preigu, Osnabrück, Germaniapreigu

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    Condizione: Nuovo

    EUR 58,05

    EUR 70,00 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 5 disponibili

    Taschenbuch. Condizione: Neu. Advances in Indexing Scientific Data | Helping Science Reach New Frontiers | Rishi Sinha | Taschenbuch | 172 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838310411 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.…

  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing, 2010

    3838310411 / 9783838310411

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    Da: Mispah books, Redhill, SURRE, Regno UnitoMispah books

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    Condizione: Usato - Come nuovo

    EUR 158,21

    EUR 29,54 spedizione 
    Spedito da Regno Unito a U.S.A.

    Quantità: 1 disponibile

    Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing Mai 2010, 2010

    3838310411 / 9783838310411

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    Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.

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    Condizione: Nuovo

    EUR 68,00

    EUR 23,00 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 2 disponibili

    Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The ability to extract information from collected data has always driven science. Today's large computers and automated sensing technologies collect terabytes of data in a few weeks. Extracting information from such large amounts of data is like trying to find a needle in a haystack. This book proposes the use of bitmap indexes to efficiently solve this problem. Earlier solutions around bitmap indexes were either too slow or too large to answer large-range scientific queries, and did not provide a way to efficiently consolidate queried data points into meaningful objects. To solve these problems, we introduce multi-resolution, adaptive bitmap indexes in this book and a novel algorithm to consolidate points into objects of interest. Data is binned at multiple granularities, and indexes created for these bins giving a 10x performance gain compared to traditional bitmaps. Making these indexes adaptive reduces the size requirement, giving a 6x performance improvement over a regular bitmap index of the same size. The consolidation algorithm uses special properties of compressed bitmaps and scientific meshes to create objects in time sub linear in number of points retrieved. 172 pp. Englisch.…

  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing, 2010

    3838310411 / 9783838310411

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    Da: moluna, Greven, Germaniamoluna

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    Condizione: Nuovo

    EUR 55,21

    EUR 48,99 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: Più di 20 disponibili

    Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sinha RishiDr. Sinha did his Ph. D.(2007) from Univ. of Illinois, Urbana inscientific data management. He has worked as a researcher at theLawrence Berkeley Lab and has many publications and patentapplications to his name. He is curr.…

  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing Mai 2010, 2010

    3838310411 / 9783838310411

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    Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000

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    Condizione: Nuovo

    EUR 68,00

    EUR 60,00 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 1 disponibile

    Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The ability to extract information from collected data has always driven science. Today's large computers and automated sensing technologies collect terabytes of data in a few weeks. Extracting information from such large amounts of data is like trying to find a needle in a haystack. This book proposes the use of bitmap indexes to efficiently solve this problem. Earlier solutions around bitmap indexes were either too slow or too large to answer large-range scientific queries, and did not provide a way to efficiently consolidate queried data points into meaningful objects. To solve these problems, we introduce multi-resolution, adaptive bitmap indexes in this book and a novel algorithm to consolidate points into objects of interest. Data is binned at multiple granularities, and indexes created for these bins giving a 10x performance gain compared to traditional bitmaps. Making these indexes adaptive reduces the size requirement, giving a 6x performance improvement over a regular bitmap index of the same size. The consolidation algorithm uses special properties of compressed bitmaps and scientific meshes to create objects in time sub linear in number of points retrieved.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 172 pp. Englisch.…

  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing, 2010

    3838310411 / 9783838310411

    • Brossura
    • Print on Demand

    Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH

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    Condizione: Nuovo

    EUR 68,00

    EUR 61,37 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 1 disponibile

    Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The ability to extract information from collected data has always driven science. Today's large computers and automated sensing technologies collect terabytes of data in a few weeks. Extracting information from such large amounts of data is like trying to find a needle in a haystack. This book proposes the use of bitmap indexes to efficiently solve this problem. Earlier solutions around bitmap indexes were either too slow or too large to answer large-range scientific queries, and did not provide a way to efficiently consolidate queried data points into meaningful objects. To solve these problems, we introduce multi-resolution, adaptive bitmap indexes in this book and a novel algorithm to consolidate points into objects of interest. Data is binned at multiple granularities, and indexes created for these bins giving a 10x performance gain compared to traditional bitmaps. Making these indexes adaptive reduces the size requirement, giving a 6x performance improvement over a regular bitmap index of the same size. The consolidation algorithm uses special properties of compressed bitmaps and scientific meshes to create objects in time sub linear in number of points retrieved.…