194,110 research outputs found
Improved Parallel Rabin-Karp Algorithm Using Compute Unified Device Architecture
String matching algorithms are among one of the most widely used algorithms
in computer science. Traditional string matching algorithms efficiency of
underlaying string matching algorithm will greatly increase the efficiency of
any application. In recent years, Graphics processing units are emerged as
highly parallel processor. They out perform best of the central processing
units in scientific computation power. By combining recent advancement in
graphics processing units with string matching algorithms will allows to speed
up process of string matching. In this paper we proposed modified parallel
version of Rabin-Karp algorithm using graphics processing unit. Based on that,
result of CPU as well as parallel GPU implementations are compared for
evaluating effect of varying number of threads, cores, file size as well as
pattern size.Comment: Information and Communication Technology for Intelligent Systems
(ICTIS 2017
String Matching with Multicore CPUs: Performing Better with the Aho-Corasick Algorithm
Multiple string matching is known as locating all the occurrences of a given
number of patterns in an arbitrary string. It is used in bio-computing
applications where the algorithms are commonly used for retrieval of
information such as sequence analysis and gene/protein identification.
Extremely large amount of data in the form of strings has to be processed in
such bio-computing applications. Therefore, improving the performance of
multiple string matching algorithms is always desirable. Multicore
architectures are capable of providing better performance by parallelizing the
multiple string matching algorithms. The Aho-Corasick algorithm is the one that
is commonly used in exact multiple string matching algorithms. The focus of
this paper is the acceleration of Aho-Corasick algorithm through a multicore
CPU based software implementation. Through our implementation and evaluation of
results, we prove that our method performs better compared to the state of the
art
Average-Case Optimal Approximate Circular String Matching
Approximate string matching is the problem of finding all factors of a text t
of length n that are at a distance at most k from a pattern x of length m.
Approximate circular string matching is the problem of finding all factors of t
that are at a distance at most k from x or from any of its rotations. In this
article, we present a new algorithm for approximate circular string matching
under the edit distance model with optimal average-case search time O(n(k + log
m)/m). Optimal average-case search time can also be achieved by the algorithms
for multiple approximate string matching (Fredriksson and Navarro, 2004) using
x and its rotations as the set of multiple patterns. Here we reduce the
preprocessing time and space requirements compared to that approach
Using string-matching to analyze hypertext navigation
A method of using string-matching to analyze hypertext navigation was developed, and evaluated using two weeks of website logfile data. The method is divided into phases that use: (i) exact string-matching to calculate subsequences of links that were repeated in different navigation sessions (common trails through the website), and then (ii) inexact matching to find other similar sessions (a community of users with a similar interest). The evaluation showed how subsequences could be used to understand the information pathways users chose to follow within a website, and that exact and inexact matching provided complementary ways of identifying information that may have been of interest to a whole community of users, but which was only found by a minority. This illustrates how string-matching could be used to improve the structure of hypertext collections
String Matching: Communication, Circuits, and Learning
String matching is the problem of deciding whether a given n-bit string contains a given k-bit pattern. We study the complexity of this problem in three settings.
- Communication complexity. For small k, we provide near-optimal upper and lower bounds on the communication complexity of string matching. For large k, our bounds leave open an exponential gap; we exhibit some evidence for the existence of a better protocol.
- Circuit complexity. We present several upper and lower bounds on the size of circuits with threshold and DeMorgan gates solving the string matching problem. Similarly to the above, our bounds are near-optimal for small k.
- Learning. We consider the problem of learning a hidden pattern of length at most k relative to the classifier that assigns 1 to every string that contains the pattern. We prove optimal bounds on the VC dimension and sample complexity of this problem
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
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