Rank-Sensitive Data Structures

Bialynicka-Birula, Iwona; Grossi, Roberto

doi:10.1007/11575832_10

Cited by 16 publications

(21 citation statements)

References 23 publications

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“…Priority search trees can be used to solve 3-sided range queries on t-point grids, returning k points in time O(k + log t). This has been used to add rank query capabilities to several index data structures such as suffix trees and range trees [29].…”

Section: Treaps Priority Search Trees and Ranked Range Queriesmentioning

confidence: 99%

Aggregated 2D range queries on clustered points

Brisaboa

Bernardo²,

Konow

et al. 2016

Information Systems

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Efficient processing of aggregated range queries on two-dimensional grids is a common requirement in information retrieval and data mining systems, for example in Geographic Information Systems and OLAP cubes. We introduce a technique to represent grids supporting aggregated range queries that requires little space when the data points in the grid are clustered, which is common in practice. We show how this general technique can be used to support two important types of aggregated queries, which are ranked range queries and counting range queries. Our experimental evaluation shows that this technique can speed up aggregated queries up to more than an order of magnitude, with a small space overhead.

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Section: Treaps Priority Search Trees and Ranked Range Queriesmentioning

confidence: 99%

Aggregated 2D range queries on clustered points

Brisaboa

Bernardo²,

Konow

et al. 2016

Information Systems

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“…From a theoretical point of view, Bialynicka-Birula and Grossi [4] introduce the notion of rank-sensitive data structures, and present a generic framework to support ranked retrieval in range-reporting data structures, such as suffix trees and tries. However, the space overhead is superlinear, which makes it impractical for our purposes.…”

Section: Related Workmentioning

confidence: 99%

Space-efficient data structures for Top- k completion

Hsu

Ottaviano

2013

Proceedings of the 22nd International Conference on World Wide Web

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Virtually every modern search application, either desktop, web, or mobile, features some kind of query auto-completion. In its basic form, the problem consists in retrieving from a string set a small number of completions, i.e. strings beginning with a given prefix, that have the highest scores according to some static ranking. In this paper, we focus on the case where the string set is so large that compression is needed to fit the data structure in memory. This is a compelling case for web search engines and social networks, where it is necessary to index hundreds of millions of distinct queries to guarantee a reasonable coverage; and for mobile devices, where the amount of memory is limited.We present three different trie-based data structures to address this problem, each one with different space/time/ complexity trade-offs. Experiments on large-scale datasets show that it is possible to compress the string sets, including the scores, down to spaces competitive with the gzip'ed data, while supporting efficient retrieval of completions at about a microsecond per completion.

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“…There has been some work on using priority search trees for returning top-k elements from suffix trees and geometric range searches [12,11] but, as far as we know, our usage of treaps for ranked queries on inverted indexes, plus their differential compression, is novel.…”

Section: The Treap Data Structurementioning

confidence: 99%

Faster and smaller inverted indices with treaps

Konow

Navarro

Clarke

et al. 2013

Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval

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We introduce a new representation of the inverted index that performs faster ranked unions and intersections while using less space. Our index is based on the treap data structure, which allows us to intersect/merge the document identifiers while simultaneously thresholding by frequency, instead of the costlier two-step classical processing methods. To achieve compression we represent the treap topology using compact data structures. Further, the treap invariants allow us to elegantly encode differentially both document identifiers and frequencies. Results show that the space consumption is below 10% of the size of the corpus and the index performs queries up to twice as fast than previous compact representations, which in addition require more space. Modern two-stage (massive filtering / detailed ranking) information retrieval systems would benefit from this boosting of the filtration stage of the query resolution process, which would free more resources for the ranking stage, thus enabling more precise results within a given time budget.

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Rank-Sensitive Data Structures

Cited by 16 publications

References 23 publications

Aggregated 2D range queries on clustered points

Aggregated 2D range queries on clustered points

Space-efficient data structures for Top- k completion

Faster and smaller inverted indices with treaps

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