earticle

논문검색

Enhanced and applicable algorithm for Big-Data by Combining Sparse Auto- Encoder and Load-Balancing, ProGReGA-KF

원문정보

초록

영어

Pervasive enhancement and required enforcement of the Internet of Things (IoTs) in a distributed massively multiplayer online architecture have effected in massive growth of Big-Data in terms of server over-load. There have been some previous works to overcome the overloading of server works. However, there are lack of considered methods, which is commonly applicable. Therefore, we propose a combing Sparse Auto-Encoder and Load-Balancing, which is ProGReGA for Big-Data of server loads. In the process of Sparse Auto-Encoder, when it comes to selection of the feature-pattern, the less relevant feature-pattern could be eliminated from Big-Data. In relation to Load-Balancing, the alleviated degradation of ProGReGA can take advantage of the less redundant feature-pattern. That means the most relevant of Big-Data representation can work. In the performance evaluation, we can find that the proposed method have become more approachable and stable.

목차

Abstract
1. INTRODUCTION
2. RELATED WORKS
3. THE PROPOSED ALGORITHM
4. PERFORMANCE EVALUATION
5. CONCLUSION
REFERENCES

저자정보

  • Hyunah Kim College of Liberal Arts and Interdisciplinary Studies, Kyonggi University
  • Chayoung Kim College of Liberal Arts and Interdisciplinary Studies, Kyonggi University

참고문헌

자료제공 : 네이버학술정보

    함께 이용한 논문

      ※ 기관로그인 시 무료 이용이 가능합니다.

      • 4,000원

      0개의 논문이 장바구니에 담겼습니다.