251,714 research outputs found
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
Regular Expression Matching and Operational Semantics
Many programming languages and tools, ranging from grep to the Java String
library, contain regular expression matchers. Rather than first translating a
regular expression into a deterministic finite automaton, such implementations
typically match the regular expression on the fly. Thus they can be seen as
virtual machines interpreting the regular expression much as if it were a
program with some non-deterministic constructs such as the Kleene star. We
formalize this implementation technique for regular expression matching using
operational semantics. Specifically, we derive a series of abstract machines,
moving from the abstract definition of matching to increasingly realistic
machines. First a continuation is added to the operational semantics to
describe what remains to be matched after the current expression. Next, we
represent the expression as a data structure using pointers, which enables
redundant searches to be eliminated via testing for pointer equality. From
there, we arrive both at Thompson's lockstep construction and a machine that
performs some operations in parallel, suitable for implementation on a large
number of cores, such as a GPU. We formalize the parallel machine using process
algebra and report some preliminary experiments with an implementation on a
graphics processor using CUDA.Comment: In Proceedings SOS 2011, arXiv:1108.279
From Regular Expression Matching to Parsing
Given a regular expression and a string , the regular expression
parsing problem is to determine if matches and if so, determine how it
matches, e.g., by a mapping of the characters of to the characters in .
Regular expression parsing makes finding matches of a regular expression even
more useful by allowing us to directly extract subpatterns of the match, e.g.,
for extracting IP-addresses from internet traffic analysis or extracting
subparts of genomes from genetic data bases. We present a new general
techniques for efficiently converting a large class of algorithms that
determine if a string matches regular expression into algorithms that
can construct a corresponding mapping. As a consequence, we obtain the first
efficient linear space solutions for regular expression parsing
Analyzing Catastrophic Backtracking Behavior in Practical Regular Expression Matching
We develop a formal perspective on how regular expression matching works in
Java, a popular representative of the category of regex-directed matching
engines. In particular, we define an automata model which captures all the
aspects needed to study such matching engines in a formal way. Based on this,
we propose two types of static analysis, which take a regular expression and
tell whether there exists a family of strings which makes Java-style matching
run in exponential time.Comment: In Proceedings AFL 2014, arXiv:1405.527
Sparse Regular Expression Matching
We present the first algorithm for regular expression matching that can take
advantage of sparsity in the input instance. Our main result is a new algorithm
that solves regular expression matching in time, where is the number of positions in
the regular expression, is the length of the string, and is the
\emph{density} of the instance, defined as the total number of active states in
a simulation of the position automaton. This measure is a lower bound on the
total number of active states in simulations of all classic polynomial sized
finite automata. Our bound improves the best known bounds for regular
expression matching by almost a linear factor in the density of the problem.
The key component in the result is a novel linear space representation of the
position automaton that supports state-set transition computation in
near-linear time in the size of the input and output state sets
Real-time Regular Expression Matching
This paper is devoted to finite state automata, regular expression matching,
pattern recognition, and the exponential blow-up problem, which is the growing
complexity of automata exponentially depending on regular expression length.
This paper presents a theoretical and hardware solution to the exponential
blow-up problem for some complicated classes of regular languages, which caused
severe limitations in Network Intrusion Detection Systems work. The article
supports the solution with theorems on correctness and complexity.Comment: 17 pages, 11 figure
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
Which Regular Expression Patterns are Hard to Match?
Regular expressions constitute a fundamental notion in formal language theory
and are frequently used in computer science to define search patterns. A
classic algorithm for these problems constructs and simulates a
non-deterministic finite automaton corresponding to the expression, resulting
in an running time (where is the length of the pattern and is
the length of the text). This running time can be improved slightly (by a
polylogarithmic factor), but no significantly faster solutions are known. At
the same time, much faster algorithms exist for various special cases of
regular expressions, including dictionary matching, wildcard matching, subset
matching, word break problem etc.
In this paper, we show that the complexity of regular expression matching can
be characterized based on its {\em depth} (when interpreted as a formula). Our
results hold for expressions involving concatenation, OR, Kleene star and
Kleene plus. For regular expressions of depth two (involving any combination of
the above operators), we show the following dichotomy: matching and membership
testing can be solved in near-linear time, except for "concatenations of
stars", which cannot be solved in strongly sub-quadratic time assuming the
Strong Exponential Time Hypothesis (SETH). For regular expressions of depth
three the picture is more complex. Nevertheless, we show that all problems can
either be solved in strongly sub-quadratic time, or cannot be solved in
strongly sub-quadratic time assuming SETH.
An intriguing special case of membership testing involves regular expressions
of the form "a star of an OR of concatenations", e.g., . This
corresponds to the so-called {\em word break} problem, for which a dynamic
programming algorithm with a runtime of (roughly) is known. We
show that the latter bound is not tight and improve the runtime to
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