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Presents new results to improve data compression by solving optimization problems
Presents efficient solutions to pattern matching problems in compressed space
Nice balance of theoretical achievements and algorithm-engineering results for compressed data structures
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.
Content Level »Research
Keywords »Compressed data structures - Data compression - Dynamic programming - Full-text indexing - Pattern matching