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Succinct Data Structures for Segments

Authors: Bille, Philip; id_orcid 0000-0002-1120-5154; Gørtz, Inge Li; id_orcid 0000-0002-8322-4952; Tarnow, Simon R.;

Succinct Data Structures for Segments

Abstract

We consider succinct data structures for representing a set of n horizontal line segments in the plane given in rank space to support segment access, segment selection, and segment rank queries. A segment access query finds the segment (x1, x2, y) given its y-coordinate (y-coordinates of the segments are distinct), a segment selection query finds the jth smallest segment (the segment with the jth smallest y-coordinate) among the segments crossing the vertical line for a given x-coordinate, and a segment rank query finds the number of segments crossing the vertical line through x-coordinate i with y-coordinate at most y, for a given x and y. This problem is a central component in compressed data structures for persistent strings supporting random access. Our main result is a data structure using 2n lg n+O(n lg n/ lg lg n) bits of space and O(lg n/ lg lg n) query time for all operations. We show that this space bound is optimal up to lower-order terms. We will also show that the query time for segment rank is optimal. The query time for segment selection is also optimal by a previous bound. To obtain our results, we present a novel segment wavelet tree data structure of independent interest. This structure is inspired by and extends the classic wavelet tree for sequences. This leads to a simple, succinct solution with O(log n) query times. We then extend this solution to obtain optimal query time. Our space lower bound follows from a simple counting argument, and our lower bound for segment rank is obtained by a reduction from 2-dimensional counting.

Country
Denmark
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Keywords

Succinct, Data structures, Selection

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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!
0
Average
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