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<http://schema.org/description>
"It has been shown in the indexing literature that there is an essential\ndifference between prefix/range searches on the one hand, and predecessor/rank\nsearches on the other hand, in that the former provably allows faster query\nresolution. Traditionally, prefix search is solved by data structures that are\nalso dictionaries---they actually contain the strings in S. For very large\ncollections stored in slow-access memory, we propose much more compact data\nstructures that support weak prefix searches---they return the ranks of\nmatching strings provided that some string in S starts with the given\nprefix. In fact, we show that our most space-efficient data structure is\nasymptotically space-optimal. Previously, data structures such as String\nB-trees (and more complicated cache-oblivious string data structures) have\nimplicitly supported weak prefix queries, but they all have query time that\ngrows logarithmically with the size of the string collection. In contrast, our\ndata structures are simple, naturally cache-efficient, and have query time that\ndepends only on the length of the prefix, all the way down to constant query\ntime for strings that fit in one machine word. We give several applications of\nweak prefix searches, including exact prefix counting and approximate counting\nof tuples matching conjunctive prefix conditions."
<http://api.deepai.org/profile/sebastiano-vigna>