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Inhaltsangabe
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.
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Reseña del editor
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.
Biografía del autor
Dr. Sinha did his Ph. D.(2007) from Univ. of Illinois, Urbana in scientific data management. He has worked as a researcher at the Lawrence Berkeley Lab and has many publications and patent applications to his name. He is currently working at Microsoft, creating novel distributed systems for SQL Azure. He lives in Bothel with his lovely wife, Swati.
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Advances in Indexing Scientific Data
Verlag:
LAP LAMBERT Academic Publishing Mai 2010, 2010
ISBN 10: 3838310411
ISBN 13: 9783838310411
Neu
Taschenbuch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Verkäuferbewertung 5 von 5 Sternen
Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9783838310411
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Advances in Indexing Scientific Data
Verlag:
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838310411
ISBN 13: 9783838310411
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Zustand: 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. Bestandsnummer des Verkäufers 5411748
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Advances in Indexing Scientific Data
Verlag:
LAP LAMBERT Academic Publishing Mai 2010, 2010
ISBN 10: 3838310411
ISBN 13: 9783838310411
Neu
Taschenbuch
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
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Taschenbuch. Zustand: 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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 172 pp. Englisch. Bestandsnummer des Verkäufers 9783838310411
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Advances in Indexing Scientific Data : Helping Science Reach New Frontiers
Verlag:
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838310411
ISBN 13: 9783838310411
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Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9783838310411
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