2 Search Results for "Vind, Soren"

Document
DOI: 10.4230/LIPIcs.ISAAC.2016.18
Dynamic Relative Compression, Dynamic Partial Sums, and Substring Concatenation

Authors: Philip Bille, Patrick Hagge Cording, Inge Li Gørtz, Frederik Rye Skjoldjensen, Hjalte Wedel Vildhøj, and Søren Vind

Published in: LIPIcs, Volume 64, 27th International Symposium on Algorithms and Computation (ISAAC 2016)

Abstract
Given a static reference string R and a source string S, a relative compression of S with respect to R is an encoding of S as a sequence of references to substrings of R. Relative compression schemes are a classic model of compression and have recently proved very successful for compressing highly-repetitive massive data sets such as genomes and web-data. We initiate the study of relative compression in a dynamic setting where the compressed source string S is subject to edit operations. The goal is to maintain the compressed representation compactly, while supporting edits and allowing efficient random access to the (uncompressed) source string. We present new data structures that achieve optimal time for updates and queries while using space linear in the size of the optimal relative compression, for nearly all combinations of parameters. We also present solutions for restricted and extended sets of updates. To achieve these results, we revisit the dynamic partial sums problem and the substring concatenation problem. We present new optimal or near optimal bounds for these problems. Plugging in our new results we also immediately obtain new bounds for the string indexing for patterns with wildcards problem and the dynamic text and static pattern matching problem.
Cite as

Philip Bille, Patrick Hagge Cording, Inge Li Gørtz, Frederik Rye Skjoldjensen, Hjalte Wedel Vildhøj, and Søren Vind. Dynamic Relative Compression, Dynamic Partial Sums, and Substring Concatenation. In 27th International Symposium on Algorithms and Computation (ISAAC 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 64, pp. 18:1-18:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@InProceedings{bille_et_al:LIPIcs.ISAAC.2016.18,
  author =	{Bille, Philip and Cording, Patrick Hagge and G{\o}rtz, Inge Li and Skjoldjensen, Frederik Rye and Vildh{\o}j, Hjalte Wedel and Vind, S{\o}ren},
  title =	{{Dynamic Relative Compression, Dynamic Partial Sums, and Substring Concatenation}},
  booktitle =	{27th International Symposium on Algorithms and Computation (ISAAC 2016)},
  pages =	{18:1--18:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-026-2},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{64},
  editor =	{Hong, Seok-Hee},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2016.18},
  URN =		{urn:nbn:de:0030-drops-67872},
  doi =		{10.4230/LIPIcs.ISAAC.2016.18},
  annote =	{Keywords: Relative compression, dynamic compression, dynamic partial sum, sub-string concatenation, external macro compression}
}
Document
DOI: 10.4230/LIPIcs.FSTTCS.2014.303
Output-Sensitive Pattern Extraction in Sequences

Authors: Roberto Grossi, Giulia Menconi, Nadia Pisanti, Roberto Trani, and Soren Vind

Published in: LIPIcs, Volume 29, 34th International Conference on Foundation of Software Technology and Theoretical Computer Science (FSTTCS 2014)

Abstract
Genomic Analysis, Plagiarism Detection, Data Mining, Intrusion Detection, Spam Fighting and Time Series Analysis are just some examples of applications where extraction of recurring patterns in sequences of objects is one of the main computational challenges. Several notions of patterns exist, and many share the common idea of strictly specifying some parts of the pattern and to don't care about the remaining parts. Since the number of patterns can be exponential in the length of the sequences, pattern extraction focuses on statistically relevant patterns, where any attempt to further refine or extend them causes a loss of significant information (where the number of occurrences changes). Output-sensitive algorithms have been proposed to enumerate and list these patterns, taking polynomial time O(n^c) per pattern for constant c > 1, which is impractical for massive sequences of very large length n. We address the problem of extracting maximal patterns with at most k don't care symbols and at least q occurrences. Our contribution is to give the first algorithm that attains a stronger notion of output-sensitivity, borrowed from the analysis of data structures: the cost is proportional to the actual number of occurrences of each pattern, which is at most n and practically much smaller than n in real applications, thus avoiding the aforementioned cost of O(n^c) per pattern.
Cite as

Roberto Grossi, Giulia Menconi, Nadia Pisanti, Roberto Trani, and Soren Vind. Output-Sensitive Pattern Extraction in Sequences. In 34th International Conference on Foundation of Software Technology and Theoretical Computer Science (FSTTCS 2014). Leibniz International Proceedings in Informatics (LIPIcs), Volume 29, pp. 303-314, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)

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@InProceedings{grossi_et_al:LIPIcs.FSTTCS.2014.303,
  author =	{Grossi, Roberto and Menconi, Giulia and Pisanti, Nadia and Trani, Roberto and Vind, Soren},
  title =	{{Output-Sensitive Pattern Extraction in Sequences}},
  booktitle =	{34th International Conference on Foundation of Software Technology and Theoretical Computer Science (FSTTCS 2014)},
  pages =	{303--314},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-77-4},
  ISSN =	{1868-8969},
  year =	{2014},
  volume =	{29},
  editor =	{Raman, Venkatesh and Suresh, S. P.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2014.303},
  URN =		{urn:nbn:de:0030-drops-48513},
  doi =		{10.4230/LIPIcs.FSTTCS.2014.303},
  annote =	{Keywords: Pattern Extraction, Motif Detection, Pattern Discovery, Motif Trie}
}
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