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Longest Common Substring Made Fully Dynamic

Authors Amihood Amir, Panagiotis Charalampopoulos , Solon P. Pissis , Jakub Radoszewski

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Author Details

Amihood Amir
  • Department of Computer Science, Bar-Ilan University, Ramat Gan, Israel
Panagiotis Charalampopoulos
  • Department of Informatics, King’s College London, London, UK, Efi Arazi School of Computer Science, The Interdisciplinary Center Herzliya, Herzliya, Israel
Solon P. Pissis
  • CWI, Amsterdam, The Netherlands
Jakub Radoszewski
  • Institute of Informatics, University of Warsaw, Warsaw, Poland, Samsung R&D Institute, Warsaw, Poland

Funding

  • Amir, Amihood: Supported by Israel Science Foundation (ISF) grant 1475/18 and United States - Israel Binational Science Foundation (BSF) grant 2018141.
  • Charalampopoulos, Panagiotis: Partially supported by Israel Science Foundation (ISF) grant 794/13.
  • Radoszewski, Jakub: Supported by the "Algorithms for text processing with errors and uncertainties" project carried out within the HOMING programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund.

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Amihood Amir, Panagiotis Charalampopoulos, Solon P. Pissis, and Jakub Radoszewski. Longest Common Substring Made Fully Dynamic. In 27th Annual European Symposium on Algorithms (ESA 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 144, pp. 6:1-6:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.ESA.2019.6

Abstract

Given two strings S and T, each of length at most n, the longest common substring (LCS) problem is to find a longest substring common to S and T. This is a classical problem in computer science with an O(n)-time solution. In the fully dynamic setting, edit operations are allowed in either of the two strings, and the problem is to find an LCS after each edit. We present the first solution to this problem requiring sublinear time in n per edit operation. In particular, we show how to find an LCS after each edit operation in O~(n^(2/3)) time, after O~(n)-time and space preprocessing. This line of research has been recently initiated in a somewhat restricted dynamic variant by Amir et al. [SPIRE 2017]. More specifically, they presented an O~(n)-sized data structure that returns an LCS of the two strings after a single edit operation (that is reverted afterwards) in O~(1) time. At CPM 2018, three papers (Abedin et al., Funakoshi et al., and Urabe et al.) studied analogously restricted dynamic variants of problems on strings. We show that the techniques we develop can be applied to obtain fully dynamic algorithms for all of these variants. The only previously known sublinear-time dynamic algorithms for problems on strings were for maintaining a dynamic collection of strings for comparison queries and for pattern matching, with the most recent advances made by Gawrychowski et al. [SODA 2018] and by Clifford et al. [STACS 2018]. As an intermediate problem we consider computing the solution for a string with a given set of k edits, which leads us, in particular, to answering internal queries on a string. The input to such a query is specified by a substring (or substrings) of a given string. Data structures for answering internal string queries that were proposed by Kociumaka et al. [SODA 2015] and by Gagie et al. [CCCG 2013] are used, along with new ones, based on ingredients such as the suffix tree, heavy-path decomposition, orthogonal range queries, difference covers, and string periodicity.

Subject Classification

ACM Subject Classification
  • Theory of computation → Pattern matching
Keywords
  • longest common substring
  • string algorithms
  • dynamic algorithms

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