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Intelligent Linear Data Structure with Self Performance Optimization Capacity: Application on Big Data

by Smail Tigani, Mouhamed Ouzzif, Abderrahim Hasbi, Rachid Saadane
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 101 - Number 6
Year of Publication: 2014
Authors: Smail Tigani, Mouhamed Ouzzif, Abderrahim Hasbi, Rachid Saadane
10.5120/17688-8635

Smail Tigani, Mouhamed Ouzzif, Abderrahim Hasbi, Rachid Saadane . Intelligent Linear Data Structure with Self Performance Optimization Capacity: Application on Big Data. International Journal of Computer Applications. 101, 6 ( September 2014), 1-6. DOI=10.5120/17688-8635

@article{ 10.5120/17688-8635,
author = { Smail Tigani, Mouhamed Ouzzif, Abderrahim Hasbi, Rachid Saadane },
title = { Intelligent Linear Data Structure with Self Performance Optimization Capacity: Application on Big Data },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 101 },
number = { 6 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume101/number6/17688-8635/ },
doi = { 10.5120/17688-8635 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:31:54.491246+05:30
%A Smail Tigani
%A Mouhamed Ouzzif
%A Abderrahim Hasbi
%A Rachid Saadane
%T Intelligent Linear Data Structure with Self Performance Optimization Capacity: Application on Big Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 101
%N 6
%P 1-6
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The access to relevant information from a big data container is gaining immense significance. This depends on storage technics and the organization level. This work proposes an intelligent linear data structure with an integrated cognitive agent reorganizing periodically the data structure content. The reorganization is based on a confidence interval of a random variable estimated by the agent. This random variable represents the demand frequency for each element. The cognitive agent studies the client behavior and puts most popular data in the beginning of the array in order to be found quickly. That increases considerably the search algorithm performance and solves by that one of most problems of the big data field. Models and algorithms in this work are implemented with Java programming language and simulated and that proves the reliability of the approach.

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Index Terms

Computer Science
Information Sciences

Keywords

Data Structures Big Data Statistic Modelling Algorithms and Computational Complexity Auto-adaptive Systems High Performance Systems.

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