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ESP-index: A compressed index based on edit-sensitive parsing

Authors: Shirou Maruyama; Masaya Nakahara; Naoya Kishiue; Hiroshi Sakamoto;

ESP-index: A compressed index based on edit-sensitive parsing

Abstract

AbstractWe propose ESP-index, a self-index based on edit-sensitive parsing. Given a string S, ESP tree is equivalent to a CFG deriving just S, which can be represented as a restricted DAG G. Finding pattern P in S is reduced to embedding the parsing tree of P into G. Adopting several succinct data structures, G is decomposed into two bit strings and a single array, requiring (1+ε)nlogn+4n+o(n) bits of space, where n is the number of variables of G and 0<ε<1. The counting time for the occurrences of P in S is in O((1/ε)(mlogn+occclogmlogu)log⁎u), where m=|P|, u=|S|, and occc is the number of the occurrences of a maximal common subtree in ESP trees of P and S. With the additional array of nlogu bits of space, our index supports the locating and extracting. Locating time is the same as counting time and extracting time for any substring of length m is O((1/ε)(m+logu)).

Keywords

Self-index, Edit-sensitive parsing, Succinct data structure, Grammar-based compression

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
23
Top 10%
Top 10%
Top 10%
hybrid