Download Combinatorial Pattern Matching: 16th Annual Symposium, CPM by Broňa Brejová, Daniel G. Brown, Ian M. Harrower (auth.), PDF

By Broňa Brejová, Daniel G. Brown, Ian M. Harrower (auth.), Alberto Apostolico, Maxime Crochemore, Kunsoo Park (eds.)

This ebook constitutes the refereed court cases of the sixteenth Annual Symposium on Combinatorial development Matching, CPM 2005, held in Jeju island, Korea on June 19-22, 2005.

The 37 revised complete papers offered have been conscientiously reviewed and chosen from 129 submissions. They represent unique examine contributions in combinatorial development matching and its functions. one of the program fields addressed are computational biology, bioinformatics, genomics, proteinomics, information compression, series research and Graphs, info retrieval, facts research, and development recognition.

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Additional resources for Combinatorial Pattern Matching: 16th Annual Symposium, CPM 2005, Jeju Island, Korea, June 19-22, 2005. Proceedings

Example text

The idea for this proof is there is actually a repeated substring of length Ω( 2k as follows: We assume that the height of the i-th error tree is larger than ci log n and prove that either there is such a substring, or that the size of the height of the (i−1)-th error tree is larger than c(i − 1) log n. Note that completely identical strings are joined since Wi are sets. Together this yields the following theorem. Theorem 3 (Average Data Structure Size and Preprocessing Time). Let n be the number of strings in S.

A linear lower bound on index size for text retrieval. In Proc. 12th ACM-SIAM Symp. on Discrete Algorithms (SODA), pages 289–294. ACM, Jan. 2001. 32 Moritz G. Maaß and Johannes Nowak 12. H. N. Gabow, J. L. Bentely, and R. E. Tarjan. Scaling and related techniques for geometry problems. In Proc. 16th ACM Symp. on Theory of Computing (STOC), pages 135–143. ACM, Apr. 1984. 13. A. Gabriele, F. Mignosi, A. Restivo, and M. Sciortino. Indexing structures for approximate string matching. In Proc. 5th Italian Conference on Algorithms and Complexity (CIAC), volume 2653 of LNCS, pages 140–151, 2003.

The number of outputs decreases from occurrence to position to document reporting. To ease further exposition, we take a unified view by considering our text database to be a set S of n strings, called the base set. The indexing problems handled by our approach are named by the structure of strings in the base set S. If S is the set of suffixes of a string T , we have the k-Approximate Text Indexing (k-ATI) problem. If S is a collection of independent strings, we have the k-Approximate Dictionary Indexing (k-ADI) problem.

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