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Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Condition: New. This volume tackles the conflicting requirements of data compression and indexing in massive datasets, by using optimization techniques to improve compression and reconfiguring data structure to increase the efficiency, and speed, of pattern-matching queries. Series: Atlantis Studies in Computing. Num Pages: 132 pages, 18 black & white illustrations, 6 black & white tables, biography. BIC Classification: UMB; UYF. Category: (P) Professional & Vocational. Dimension: 167 x 241 x 13. Weight in Grams: 368. . 2013. 2014th Edition. hardcover. . . . .
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Add to basketHardcover. Condition: Brand New. 2014 edition. 150 pages. 9.25x6.10x0.50 inches. In Stock.
Condition: New. This volume tackles the conflicting requirements of data compression and indexing in massive datasets, by using optimization techniques to improve compression and reconfiguring data structure to increase the efficiency, and speed, of pattern-matching queries. Series: Atlantis Studies in Computing. Num Pages: 132 pages, 18 black & white illustrations, 6 black & white tables, biography. BIC Classification: UMB; UYF. Category: (P) Professional & Vocational. Dimension: 167 x 241 x 13. Weight in Grams: 368. . 2013. 2014th Edition. hardcover. . . . . Books ship from the US and Ireland.
Language: English
Published by Atlantis Press Nov 2013, 2013
ISBN 10: 9462390320 ISBN 13: 9789462390324
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data compression is mandatory to manage massive datasets, indexing is fundamental to query them. However, their goals appear as counterposed: the former aims at minimizing data redundancies, whereas the latter augments the dataset with auxiliary information to speed up the query resolution. In this monograph we introduce solutions that overcome this dichotomy. We start by presenting the use of optimization techniques to improve the compression of classical data compression algorithms, then we move to the design of compressed data structures providing fast random access or efficient pattern matching queries on the compressed dataset. These theoretical studies are supported by experimental evidences of their impact in practical scenarios. 132 pp. Englisch.
Seller: moluna, Greven, Germany
Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents new results to improve data compression by solving optimization problemsPresents efficient solutions to pattern matching problems in compressed spaceNice balance of theoretical achievements and algorithm-engineering results for com.
Language: English
Published by Atlantis Press Nov 2013, 2013
ISBN 10: 9462390320 ISBN 13: 9789462390324
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Data compression is mandatory to manage massive datasets, indexing is fundamental to query them. However, their goals appear as counterposed: the former aims at minimizing data redundancies, whereas the latter augments the dataset with auxiliary information to speed up the query resolution. In this monograph we introduce solutions that overcome this dichotomy. We start by presenting the use of optimization techniques to improve the compression of classical data compression algorithms, then we move to the design of compressed data structures providing fast random access or efficient pattern matching queries on the compressed dataset. These theoretical studies are supported by experimental evidences of their impact in practical scenarios.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 132 pp. Englisch.
Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Data compression is mandatory to manage massive datasets, indexing is fundamental to query them. However, their goals appear as counterposed: the former aims at minimizing data redundancies, whereas the latter augments the dataset with auxiliary information to speed up the query resolution. In this monograph we introduce solutions that overcome this dichotomy. We start by presenting the use of optimization techniques to improve the compression of classical data compression algorithms, then we move to the design of compressed data structures providing fast random access or efficient pattern matching queries on the compressed dataset. These theoretical studies are supported by experimental evidences of their impact in practical scenarios.