Conference Paper: Compressed index for dynamic text

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TitleCompressed index for dynamic text
AuthorsHon, WK2
Lam, TW2
Sadakane, K1
Sung, WK3
Yiu, SM2
Issue Date2004
CitationData Compression Conference Proceedings, 2004, p. 102-111 [How to Cite?]
AbstractThis paper investigates how to index a text which is subject to updates. The best solution in the literature is based on suffix tree using O(n log n) bits of storage, where n is the length of the text. It supports finding all occurrences of a pattern P in O(|P| + occ) time, where occ is the number of occurrences. Each text update consists of inserting or deleting a substring of length y and can be supported in O(y + √n) time. In this paper, we initiate the study of compressed index using only O(n log |Σ|) bits of space, where Σ denotes the alphabet. Our solution supports finding all occurrences of a pattern P in O(|P|log2 n(logε n + log |ΣE|) + occ log1+ε n) time, while insertion or deletion of a substring of length y can be done in O((y + √n) log 2+εn) amortized time, where 0 < ε ≤ 1. The core part of our data structure is based on the recent work on Compressed Suffix Trees (CST) and Compressed Suffix Arrays (CSA).
ISSN1068-0314
2011 SCImago Journal Rankings: 0.045
ReferencesReferences in Scopus
DC Field
Value
dc.contributor.authorHon, WK
dc.contributor.authorLam, TW
dc.contributor.authorSadakane, K
dc.contributor.authorSung, WK
dc.contributor.authorYiu, SM
dc.date.accessioned2012-06-26T06:30:14Z
dc.date.available2012-06-26T06:30:14Z
dc.date.issued2004
dc.description.abstractThis paper investigates how to index a text which is subject to updates. The best solution in the literature is based on suffix tree using O(n log n) bits of storage, where n is the length of the text. It supports finding all occurrences of a pattern P in O(|P| + occ) time, where occ is the number of occurrences. Each text update consists of inserting or deleting a substring of length y and can be supported in O(y + √n) time. In this paper, we initiate the study of compressed index using only O(n log |Σ|) bits of space, where Σ denotes the alphabet. Our solution supports finding all occurrences of a pattern P in O(|P|log2 n(logε n + log |ΣE|) + occ log1+ε n) time, while insertion or deletion of a substring of length y can be done in O((y + √n) log 2+εn) amortized time, where 0 < ε ≤ 1. The core part of our data structure is based on the recent work on Compressed Suffix Trees (CST) and Compressed Suffix Arrays (CSA).
dc.description.natureLink_to_subscribed_fulltext
dc.identifier.citationData Compression Conference Proceedings, 2004, p. 102-111 [How to Cite?]
dc.identifier.epage111
dc.identifier.issn1068-0314
2011 SCImago Journal Rankings: 0.045
dc.identifier.scopuseid_2-s2.0-2642533893
dc.identifier.spage102
dc.identifier.urihttp://hdl.handle.net/10722/151868
dc.languageeng
dc.relation.ispartofData Compression Conference Proceedings
dc.relation.referencesReferences in Scopus
dc.titleCompressed index for dynamic text
dc.typeConference_Paper
Author Affiliations
  1. Kyushu University
  2. The University of Hong Kong
  3. National University of Singapore