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What and Who
Title:Local Decodability of the Burrows-Wheeler Transform
Speaker:Sandip Sinha
coming from:Columbia University
Speakers Bio:
Event Type:AG1 Mittagsseminar (own work)
Visibility:D1, MMCI
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Level:AG Audience
Language:English
Date, Time and Location
Date:Tuesday, 18 June 2019
Time:13:00
Duration:45 Minutes
Location:Saarbr├╝cken
Building:E1 4
Room:024
Abstract
The Burrows-Wheeler Transform (BWT) is a reversible preprocessing step that facilitates significant compression of strings with context, and is the basis of the bzip compression program. Alas, the decoding process of BWT is inherently sequential and requires \Omega(n) time even to retrieve a single character.

We study the succinct data structure problem of locally decoding short substrings of a given text under its compressed BWT, i.e., with small additive redundancy r over the Move-To-Front compression. The celebrated BWT-based FM-index yield a trade-off of r = O~(n/\sqrt{t}) bits, to decode a single character in O(t) time. We give a near-quadratic improvement r = O~(n\lg(t)/t). As a by-product, we obtain an exponential (in t) improvement on the redundancy of the FM-index for counting pattern-matches on compressed text. In the interesting regime where the text compresses to n^{1-o(1)} bits, these results provide an exp(t) overall space reduction. For the local decoding problem of BWT, we also prove an \Omega(n/t^2) cell-probe lower bound for "symmetric" data structures.

We achieve our main result by designing a compressed partial-sums (Rank) data structure over BWT. The key component is a locally-decodable Move-to-Front (MTF) code: with only O(1) extra bits per block of length n^{\Omega(1)}, the decoding time of a single character can be decreased from \Omega(n) to O(\lg n). This result is of independent interest in algorithmic information theory.

This is joint work with Omri Weinstein.

Note: This talk will take 45min.

Contact
Name(s):Karl Bringmann
Video Broadcast
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Created:
Karl Bringmann, 04/18/2019 10:29 AM
Last modified:
Uwe Brahm/MPII/DE, 05/14/2019 07:01 AM
  • Karl Bringmann, 05/12/2019 11:03 AM
  • Karl Bringmann, 04/18/2019 10:29 AM -- Created document.