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Locally Compressed Suffix Arrays
"... Compressed text (self)indexes have matured up to a point where they can replace a text by a data structure that requires less space and, in addition to giving access to arbitrary text passages, support indexed text searches. At this point those indexes are competitive with traditional text indexes ..."
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Cited by 2 (2 self)
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(which are very large) for counting the number of occurrences of a pattern in the text. Yet, they are still hundreds to thousands of times slower when it comes to locating those occurrences in the text. In this paper we introduce a new, local, compression scheme for suffix arrays which permits locating
Compressed Suffix Arrays for Massive Data
"... Abstract. We present a fast spaceefficient algorithm for constructing compressed suffix arrays (CSA). The algorithm requires O(n logn) time in the worst case, and only O(n) bits of extra space in addition to the CSA. As the basic step, we describe an algorithm for merging two CSAs. We show that the ..."
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Abstract. We present a fast spaceefficient algorithm for constructing compressed suffix arrays (CSA). The algorithm requires O(n logn) time in the worst case, and only O(n) bits of extra space in addition to the CSA. As the basic step, we describe an algorithm for merging two CSAs. We show
Compressed Suffix Arrays for Massive Data
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Please read them carefully. I have read and I understand the following statement: All material supplied via HELDA is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you for your research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user.
Approximate string matching using compressed suffix arrays
 In Proceedings of Symposium on Combinatorial Pattern Matching
, 2004
"... Abstract. Let T be a text of length n and P be a pattern of length m, bothstrings over a fixed finite alphabet A. The kdifference (kmismatch, respectively)problem is to find all occurrences of P in T that have edit distance (Hammingdistance, respectively) at most k from P. In this paper we investi ..."
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Cited by 26 (2 self)
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. The best previous resultrequires O(n log n) bits indexing data structure and gives O(Akmk+2 + occ)query time. Our solution also allows us to exploit compressed suffix arrays to reduce the indexing space to O(n) bits, while increasing the query time by an O(log n) factor only. 1 Introduction Let T be a
Improved and Extended Locating Functionality on Compressed Suffix Arrays
"... Compressed Suffix Arrays (CSAs) offer the same functionality as classical suffix arrays (SAs), and more, within space close to that of the compressed text, and in addition they can reproduce any text fragment. Furthermore, their pattern search times are comparable to those of SAs. This combination h ..."
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Compressed Suffix Arrays (CSAs) offer the same functionality as classical suffix arrays (SAs), and more, within space close to that of the compressed text, and in addition they can reproduce any text fragment. Furthermore, their pattern search times are comparable to those of SAs. This combination
Compressed suffix arrays and suffix trees with applications to text indexing and string matching
, 2005
"... The proliferation of online text, such as found on the World Wide Web and in online databases, motivates the need for spaceefficient text indexing methods that support fast string searching. We model this scenario as follows: Consider a text T consisting of n symbols drawn from a fixed alphabet Σ. ..."
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Cited by 239 (19 self)
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new text index that is based upon compressed representations of suffix arrays and suffix trees. It achieves a fast O(m / lg Σ  n +lgɛ Σ  n) search time in the worst case, for any constant
Compressed text databases with efficient query algorithms based on the compressed suffix array
 Proceedings of ISAAC'00, number 1969 in LNCS
, 2000
"... A compressed text database based on the compressed suffix array is proposed. The compressed su#x array of Grossi and Vitter occupies only O(n) bits for a text of length n; however it also uses the text itself that occupies O(n log bits for the alphabet #. On the other hand, our data structure does n ..."
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Cited by 69 (3 self)
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A compressed text database based on the compressed suffix array is proposed. The compressed su#x array of Grossi and Vitter occupies only O(n) bits for a text of length n; however it also uses the text itself that occupies O(n log bits for the alphabet #. On the other hand, our data structure does
Constructing compressed suffix arrays with large alphabets
 In Proc. ISAAC’03, LNCS 2906
, 2003
"... , and WingKin Sung 3 1 ..."
When indexing equals compression: Experiments with compressing suffix arrays and applications
, 2004
"... We report on a new and improved version of highorder entropycompressed suffix arrays, which has theoretical performance guarantees similar to those in our earlier work [16], yet represents an improvement in practice. Our experiments indicate that the resulting text index offers stateoftheart co ..."
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Cited by 46 (5 self)
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We report on a new and improved version of highorder entropycompressed suffix arrays, which has theoretical performance guarantees similar to those in our earlier work [16], yet represents an improvement in practice. Our experiments indicate that the resulting text index offers state
Algorithms on Strings Based on the Compressed Suffix Arrays
"... this paper we propose compressed text databases and query algorithms using the compressed su#x array of Grossi and Vitter. We support three basic operations for text databases, inverse, search and decompress, without using the text itself. Since we do not need the original text, the size of our dat ..."
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this paper we propose compressed text databases and query algorithms using the compressed su#x array of Grossi and Vitter. We support three basic operations for text databases, inverse, search and decompress, without using the text itself. Since we do not need the original text, the size of our
Results 1  10
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