Titlebook: String Processing and Information Retrieval; 9th International Sy Alberto H. F. Laender,Arlindo L. Oliveira Conference proceedings 2002 Spr

IVORY

Stemming Galician Texts over documents previous to the appearance of the first Galician orthographic regulation in 1977; therefore we have adopted an ., consisting in defining a huge collection of . for allowing systematic word comparisons, to stem documents written before that date. We also describe here a tool to build the wordsets needed in this approach.

NIB

元音

巩固

增减字母法

The DBLP Computer Science Bibliography: Evolution, Research Issues, Perspectivesearned societies or commercial publishers, and a huge number of web sites maintained by the scientists themselves or their institutions. Comprehensive meta-indices for this increasing number of information sources are missing for most areas of science. The DBLP Computer Science Bibliography of the U

倒转

From Searching Text to Querying XML Streamssed routing or selective dissemination of information, require thousands or millions of XPath expressions to be evaluated simultaneously on the incoming XML stream at a high, sustained rate.Conceptually, the XPath evaluation problem is analogous to the text search problem, in which one or several re

Entirety

String Matching Problems from Bioinformatics Which Still Need Better Solutionssearching community. We will describe two problems arising from Bioinformatics, their preliminary solutions, and the more general problem that they pose. We hope that this will encourage researchers to find better, general, algorithms to solve these problems.

fastness

Optimal Exact String Matching Based on Suffix Arrayshe type “Where are all . occurrences of . in .?” can be answered in .(|.|+.) time, totally independent of the size of .. However, in large scale applications as genome analysis, the space requirements of the suffix tree are a severe drawback. The suffix array is a more space economical index structu

Humble

Faster String Matching with Super-Alphabetsharacter comparisons on average, as shown in [.]. Consequently, it is believed that this lower bound implies also an Ω . log.. lower bound for the execution time of an optimal algorithm. However, in this paper we show how to obtain an . average time algorithm. This is achieved by slightly changing t

Inclement