A bit-string longest-common-subsequence algorithm
Information Processing Letters
A new approach to text searching
Communications of the ACM
Text algorithms
A fast bit-vector algorithm for approximate string matching based on dynamic programming
Journal of the ACM (JACM)
A fast string searching algorithm
Communications of the ACM
Efficient string matching: an aid to bibliographic search
Communications of the ACM
A technique for computer detection and correction of spelling errors
Communications of the ACM
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
Fast and flexible string matching by combining bit-parallelism and suffix automata
Journal of Experimental Algorithmics (JEA)
NR-grep: a fast and flexible pattern-matching tool
Software—Practice & Experience
A fast and practical bit-vector algorithm for the longest common subsequence problem
Information Processing Letters
Flexible pattern matching in strings: practical on-line search algorithms for texts and biological sequences
SPIRE 2002 Proceedings of the 9th International Symposium on String Processing and Information Retrieval
Faster Bit-Parallel Approximate String Matching
CPM '02 Proceedings of the 13th Annual Symposium on Combinatorial Pattern Matching
Flexible Framework for Time-Series Pattern Matching over Multi-dimension Data Stream
New Frontiers in Applied Data Mining
Efficient longest common subsequence computation using bulk-synchronous parallelism
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part V
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There exist practical bit-parallel algorithms for several types of pair-wise string processing, such as longest common subsequence computation or approximate string matching. The bit-parallel algorithms typically use a size-σ table of match bit-vectors, where the bits in the vector for a character λ identify the positions where the character λ occurs in one of the processed strings, and σ is the alphabet size. The time or space cost of computing the match table is not prohibitive with reasonably small alphabets such as ASCII text. However, for example in the case of general Unicode text the possible numerical code range of the characters is roughly one million. This makes using a simple table impractical. In this paper we evaluate three different schemes for overcoming this problem. First we propose to replace the character code table by a character code automaton. Then we compare this method with two other schemes: using a hash table, and the binary-search based solution proposed by Wu, Manber and Myers [25]. We find that the best choice is to use either the automaton-based method or a hash table.