Earticle

초록

영어

Big Data is becoming more and more significant these years since our daily life is facing huge number of data as the millions of electronic devices. Big Data is not only with the huge volume or size, but also with the high complexity. This paper presents a multi-dimensional matrix model for analyzing the large text datasets based on the attributes, which come from the key words from the texts. These key words form an N dimensional space. Thus, the individual information could be presented by an M×N matrix. The multi-dimensional matrix approach has been compared with GA and PSO algorithm so as to test the efficiency and effectiveness of different approaches on analyzing the text datasets. From the experiments, it is observed that the proposed approach outperforms GA and PSO in sufficiency and computational cost. Some key findings are: For high dimensional Big Text Data, at the beginning, PSO has the best sufficiency from 0 to 10. After that, from 10 to 1000, the prosed multi-dimensional matrix approach significantly outperforms GA and PSO. For Connect-4 data samples, the time cost of proposed approach is only 352153.6 unit of time, while GA takes 613601.4 which is more of about half the time cost and PSO takes 469464.1.

목차

Abstract
 1. Introduction
 2. Problem Description
 3. Multi-dimensional Matrix and its Applications
  3.1. Several Definitions
  3.2. Characteristics of Multi-dimensional Matrix
  3.3. A Typical Application on Image Big Data Processing
 4. Experiments and Discussions
 5. Summary
 References

키워드

저자

• Fan Linxiu [ School of Mathematics and Computer Science; Gannan Normal University, Ganzhou, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605