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Shiftor string matching with superalphabets
 In: Proceedings of the 9th International Symposium Symposium on String Processing and Information Retrieval (SPIRE'2002). LNCS 2476
, 2002
"... Given a text T[1...n] and a pattern P[1...m] over some alphabet Σ of size σ, we want to find all the (exact) occurrences of P in T. The wellknown shiftor algorithm solves this problem in time O(n⌈m/w⌉), where w is the number of bits in machine word, using bitparallelism. We show how to extend the ..."
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Cited by 10 (3 self)
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Given a text T[1...n] and a pattern P[1...m] over some alphabet Σ of size σ, we want to find all the (exact) occurrences of P in T. The wellknown shiftor algorithm solves this problem in time O(n⌈m/w⌉), where w is the number of bits in machine word, using bitparallelism. We show how to extend the bitparallelism in another direction, using superalphabets. This gives a speedup by a factor s, where s is the number of characters processed simultaneously. The algorithm is implemented, and we show that it works well in practice too. The result is the fastest known algorithm for exact string matching for short patterns and small alphabets. Key words: Algorithms, bitparallelism, string matching
Optimal exact and fast approximate two dimensional pattern matching allowing rotations
 In Proc. 13th Annual Symposium on Combinatorial Pattern Matching (CPM 2002), LNCS 2373
, 2002
"... Abstract. We give fast filtering algorithms to search for a 2 dimensional pattern in a 2dimensional text allowing any rotation of the pattern. We consider the cases of exact and approximate matching under several matching models, improving the previous results. For a text of size n \Theta n charac ..."
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Cited by 6 (2 self)
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Abstract. We give fast filtering algorithms to search for a 2 dimensional pattern in a 2dimensional text allowing any rotation of the pattern. We consider the cases of exact and approximate matching under several matching models, improving the previous results. For a text of size n \Theta n characters and a pattern of size m \Theta m characters, the exact matching takes average time O(n2 log m=m2), which is optimal. If we allow k mismatches of characters, then our best algorithm achieves O(n2k log m=m2) average time, for reasonable k values. For large k, we obtain an O(n2k3=2 p log m=m) average time algorithm. We generalize
Efficient Evaluation of Parameterized Pattern Queries
 In Proc. Intl. Conf. on Information and Knowledge Management (CIKM
, 2005
"... Many applications rely on sequence databases and use extensively patternmatching queries to retrieve data of interest. This paper extends the traditional patternmatching expressions to parameterized patterns, featuring variables. Parameterized patterns are more expressive and allow to define conci ..."
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Cited by 5 (2 self)
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Many applications rely on sequence databases and use extensively patternmatching queries to retrieve data of interest. This paper extends the traditional patternmatching expressions to parameterized patterns, featuring variables. Parameterized patterns are more expressive and allow to define concisely regular expressions that would be very complex to describe without variables. They can also be used to express additional constraints on patterns' variables.
Sequential and indexed twodimensional combinatorial template matching allowing rotations
 THEORETICAL COMPUTER SCIENCE A
, 2005
"... We present new and faster algorithms to search for a 2dimensional pattern in a 2dimensional text allowing any rotation of the pattern. This has applications such as image databases and computational biology. We consider the cases of exact and approximate matching under several matching models, usi ..."
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Cited by 3 (2 self)
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We present new and faster algorithms to search for a 2dimensional pattern in a 2dimensional text allowing any rotation of the pattern. This has applications such as image databases and computational biology. We consider the cases of exact and approximate matching under several matching models, using a combinatorial approach that generalizes string matching techniques. We focus on sequential algorithms, where only the pattern can be preprocessed, as well as on indexed algorithms, where the text is preprocessed and an index built on it. On sequential searching we derive averagecase lower bounds and then obtain optimal averagecase algorithms for all the matching models. At the same time, these algorithms are worstcase optimal. On indexed searching we obtain search time polylogarithmic on the text size, as well as sublinear time in general for approximate searching.
Fast Multipattern Matching for Intrusion Detection
 in "13th Annual EICAR Conference (European Institute for Computer AntiVirus Research) CDrom: Best Paper Proceedings", U. E. GATTIKER (editor)., EICAR Best Paper Proceedings CDrom (ISBN: 8798727168
, 2004
"... M. Rusinowitch is senior researcher at INRIA. He got a Ph.D. in Computer Science at Nancy in 1987. He is now leader of the CASSIS research team of INRIALorraine with about 20 members, whose activities are focused on automated deduction, software verification and security. M. Rusinowitch’s research ..."
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Cited by 1 (0 self)
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M. Rusinowitch is senior researcher at INRIA. He got a Ph.D. in Computer Science at Nancy in 1987. He is now leader of the CASSIS research team of INRIALorraine with about 20 members, whose activities are focused on automated deduction, software verification and security. M. Rusinowitch’s research is concerned with the automated detection of flaws in software using symbolic analysis techniques. He is the author or coauthor of more than 22 papers in journals and 50 papers in conference and is the author of a book. He is also cochairman of the next IJCAR conference to be held in 2004 at Cork and PC member of several events in automated deduction and security.
Faster String Matching With Super{alphabets
 Information Processing Letters
, 2002
"... Given a text T [1 : : : n] and a pattern P [1 : : : m] over some alphabet of size , nding the exact occurrences of P in T requires at least (n log m=m) character comparisons on average, as shown in [19]. Consequently, it is believed that this lower bound implies also an (n log m=m) lower ..."
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Given a text T [1 : : : n] and a pattern P [1 : : : m] over some alphabet of size , nding the exact occurrences of P in T requires at least (n log m=m) character comparisons on average, as shown in [19]. Consequently, it is believed that this lower bound implies also an (n log m=m) lower bound for the execution time of an optimal algorithm. However, in this paper we show how to obtain an O(n=m) average time algorithm. This is achieved by slightly changing the model of computation, and with a modi cation of an existing algorithm. Our technique uses a super{alphabet for simulating sux automaton. The space usage of the algorithm is O(m). The technique can be applied to many other string matching algorithms, including dictionary matching, which is also solved in expected time O(n=m), and approximate matching allowing k edit operations (mismatches, insertions or deletions of characters) . This is solved in expected time O(nk=m) for k O(m= log m).
The 27th Workshop on Combinatorial Mathematics and Computation Theory Algorithms for the Hybrid Constrained Longest Common Subsequence Problem
"... We investigate a variant of the longest common subsequence problem. Given two sequences X, Y and two constrained patterns P, Q of lengths m, n, p, and q, respectively, the hybrid constrained longest common subsequence problem is to find a longest common subsequence of X and Y such that the resulting ..."
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We investigate a variant of the longest common subsequence problem. Given two sequences X, Y and two constrained patterns P, Q of lengths m, n, p, and q, respectively, the hybrid constrained longest common subsequence problem is to find a longest common subsequence of X and Y such that the resulting LCS is both a supersequence of P and a nonsupersequence of Q. Without loss of generality, assume that m ≤ n. We present a new dynamic programming algorithm for solving this problem in O(mnpq) time and space. We also propose another algorithm by restricting the computation on the positions of matches between X and Y. The latter algorithm requires O(pqr log log n + n log n) time over an infinite alphabet and O((pqr+n) log log n)) time over a finite alphabet, and O(pq(r + n)) space for both cases, where r denotes the total number of matches between X and Y. 1