Parallel String Matching with Linear Array, Butterfly and Divide and Conquer Models

S. Viswanadha Raju, K. K. V. V. S. Reddy and Chinta Someswara Rao ()
Additional contact information
S. Viswanadha Raju: JNT University Hyderabad
K. K. V. V. S. Reddy: Rayalasheema University
Chinta Someswara Rao: SRKR Engineering College

Annals of Data Science, 2018, vol. 5, issue 2, No 4, 207 pages

Abstract: Abstract String Matching is a technique of searching a pattern in a text. It is the basic concept to extract the fruitful information from large volume of text, which is used in different applications like text processing, information retrieval, text mining, pattern recognition, DNA sequencing and data cleaning etc., . Though it is stated some of the simple mechanisms perform very well in practice, plenty of research has been published on the subject and research is still active in this area and there are ample opportunities to develop new techniques. For this purpose, this paper has proposed linear array based string matching, string matching with butterfly model and string matching with divide and conquer models for sequential and parallel environments. To assess the efficiency of the proposed models, the genome sequences of different sizes (10–100 Mb) are taken as input data set. The experimental results have shown that the proposed string matching algorithms performs very well compared to those of Brute force, KMP and Boyer moore string matching algorithms.

Keywords: String matching; Parallel matching; DNA; Array based string matching model; String matching with butterfly model; String matching with divide and conquer model (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40745-017-0124-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:aodasc:v:5:y:2018:i:2:d:10.1007_s40745-017-0124-1

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/40745

DOI: 10.1007/s40745-017-0124-1

Access Statistics for this article

Annals of Data Science is currently edited by Yong Shi

More articles in Annals of Data Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().