Titlebook: Compressed Data Structures for Strings; On Searching and Ext Rossano Venturini Book 2014 Atlantis Press and the authors 2014 Compressed dat

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Dictionary Indexes,l down to some basic algorithmic primitives which involve a large dictionary of strings having variable length. Typical examples include: pattern matching (exact, approximate, with wild-cards,?.), the ranking of a string in a sorted dictionary, or the selection of the .-th string from it. While it i

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Dictionary Indexes,r uncommon. However they are quite often used (probably, unconsciously!) by programmers to replace long strings with unique . which are easier and faster to be processed and compressed. In this context ranking a string means mapping it to its unique ., whereas selecting the .-th string means retrieving it from its . (i.e. its ranked position .

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https://doi.org/10.1007/978-3-8350-9346-1ion of the compressed data. In these results, compressed space usually means space close to the -th order empirical entropy of , and efficient time means something depending on the length of the searched string.

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2212-8557 in compressed space.Nice balance of theoretical achievementsData 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 auxi

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Book 2014er 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

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