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Top-\(k\) term-proximity in succinct space. (English) Zbl 1366.68039
Ahn, Hee-Kap (ed.) et al., Algorithms and computation. 25th international symposium, ISAAC 2014, Jeonju, Korea, December 15–17, 2014. Proceedings. Cham: Springer (ISBN 978-3-319-13074-3/pbk; 978-3-319-13075-0/ebook). Lecture Notes in Computer Science 8889, 169-180 (2014).
Summary: Let \(\mathcal {D} = \{\mathsf {T}_1,\mathsf {T}_2, \dots ,\mathsf {T}_D\}\) be a collection of \(D\) string documents of \(n\) characters in total, that are drawn from an alphabet set \(\Sigma =[\sigma]\). The top-\(k\) document retrieval problem is to preprocess \(\mathcal{D}\) into a data structure that, given a query \((P[1..p],k)\), can return the \(k\) documents of \(\mathcal{D}\) most relevant to pattern \(P\). The relevance is captured using a predefined ranking function, which depends on the set of occurrences of \(P\) in \(\mathsf {T}_d\). For example, it can be the term frequency (i.e., the number of occurrences of \(P\) in \(\mathsf {T}_d\)), or it can be the term proximity (i.e., the distance between the closest pair of occurrences of \(P\) in \(\mathsf {T}_d\)), or a pattern-independent importance score of \(\mathsf {T}_d\) such as PageRank. Linear space and optimal query time solutions already exist for this problem. Compressed and compact space solutions are also known, but only for a few ranking functions such as term frequency and importance. However, space efficient data structures for term proximity based retrieval have been evasive. In this paper we present the first sub-linear space data structure for this relevance function, which uses only \(o(n)\) bits on top of any compressed suffix array of \(\mathcal{D}\) and solves queries in time \(O((p+k) \operatorname{polylog}n)\).
For the entire collection see [Zbl 1318.68007].

MSC:
68P20 Information storage and retrieval of data
68P05 Data structures
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