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.
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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.
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Buch. Condición: 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. Nº de ref. del artículo: 9789462390324
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Condición: 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. . . . . Nº de ref. del artículo: V9789462390324
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Condición: 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. Nº de ref. del artículo: V9789462390324
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Condición: 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. Nº de ref. del artículo: 5839553
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