153,105 research outputs found
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
A Compact Index for Order-Preserving Pattern Matching
Order-preserving pattern matching was introduced recently but it has already
attracted much attention. Given a reference sequence and a pattern, we want to
locate all substrings of the reference sequence whose elements have the same
relative order as the pattern elements. For this problem we consider the
offline version in which we build an index for the reference sequence so that
subsequent searches can be completed very efficiently. We propose a
space-efficient index that works well in practice despite its lack of good
worst-case time bounds. Our solution is based on the new approach of
decomposing the indexed sequence into an order component, containing ordering
information, and a delta component, containing information on the absolute
values. Experiments show that this approach is viable, faster than the
available alternatives, and it is the first one offering simultaneously small
space usage and fast retrieval.Comment: 16 pages. A preliminary version appeared in the Proc. IEEE Data
Compression Conference, DCC 2017, Snowbird, UT, USA, 201
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