7,004 research outputs found
Improved Approximate String Matching and Regular Expression Matching on Ziv-Lempel Compressed Texts
We study the approximate string matching and regular expression matching
problem for the case when the text to be searched is compressed with the
Ziv-Lempel adaptive dictionary compression schemes. We present a time-space
trade-off that leads to algorithms improving the previously known complexities
for both problems. In particular, we significantly improve the space bounds,
which in practical applications are likely to be a bottleneck
Fast and Compact Regular Expression Matching
We study 4 problems in string matching, namely, regular expression matching,
approximate regular expression matching, string edit distance, and subsequence
indexing, on a standard word RAM model of computation that allows
logarithmic-sized words to be manipulated in constant time. We show how to
improve the space and/or remove a dependency on the alphabet size for each
problem using either an improved tabulation technique of an existing algorithm
or by combining known algorithms in a new way
Faster subsequence recognition in compressed strings
Computation on compressed strings is one of the key approaches to processing
massive data sets. We consider local subsequence recognition problems on
strings compressed by straight-line programs (SLP), which is closely related to
Lempel--Ziv compression. For an SLP-compressed text of length , and an
uncompressed pattern of length , C{\'e}gielski et al. gave an algorithm for
local subsequence recognition running in time . We improve
the running time to . Our algorithm can also be used to
compute the longest common subsequence between a compressed text and an
uncompressed pattern in time ; the same problem with a
compressed pattern is known to be NP-hard
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