Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
권호 표지
DOI QR코드

DOI QR Code

An Efficient data management Scheme for Hierarchical Multi-processing using Double Hash Chain

이중 해쉬체인을 이용한 계층적 다중 처리를 위한 효율적인 데이터 관리 기법

  • Jeong, Yoon-Su (Dept. of Information and Communication Convergence engineering, Mokwon University) ;
  • Kim, Yong-Tae (Dept. of Multimedia Engineering, Hannam, University) ;
  • Park, Gil-Cheol (Dept. of Multimedia Engineering, Hannam, University)
  • 정윤수 (목원대학교 정보통신융합공학부) ;
  • 김용태 (한남대학교 멀티미디어학부) ;
  • 박길철 (한남대학교 멀티미디어학부)
  • Received : 2015.08.19
  • Accepted : 2015.10.20
  • Published : 2015.10.28
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, bit data is difficult to easily collect the desired data because big data is collected via the Internet. Big data is higher than the rate at which the data type and the period of time for which data is collected depending on the size of data increases. In particular, since the data of all different by the intended use and the type of data processing accuracy and computational cost is one of the important items. In this paper, we propose data processing method using a dual-chain in a manner to minimize the computational cost of the data when data is correctly extracted at the same time a multi-layered process through the desired number of the user and different kinds of data on the Internet. The proposed scheme is classified into a hierarchical data in accordance with the intended use and method to extract various kinds of data. At this time, multi-processing and tie the data hash with the double chain to enhance the accuracy of the reading. In addition, the proposed method is to organize the data in the hash chain for easy access to the hierarchically classified data and reduced the cost of processing the data. Experimental results, the proposed method is the accuracy of the data on average 7.8% higher than conventional techniques, processing costs were reduced by 4.9% of the data.

현재 인터넷을 통해 수집되는 빅 데이터는 데이터의 종류와 크기에 따라 데이터가 수집되는 시간보다 데이터가 증가하는 속도가 높아 사용자가 원하는 데이터를 원활하게 수집하는 것이 어려운 상황이다. 특히, 데이터의 사용 목적 및 종류에 따라 다르게 처리되기 때문에 데이터의 정확성과 계산비용이 빅 데이터 관리에 중요한 항목 중 하나이다. 본 논문에서는 인터넷에 존재하는 수많은 서로 다른 종류의 데이터를 사용자가 원할 때, 데이터를 정확하게 추출하는 동시에 데이터의 계산비용을 최소화하기 위해서 이중 해쉬체인을 이용한 계층적 다중처리 기반의 데이터 처리기법을 제안한다. 제안 기법은 다양한 종류의 데이터를 추출하기 위해서 데이터를 사용 목적 및 방법에 따라 계층적으로 분류한다. 이때, 데이터의 정확도를 높이기 위해서 데이터를 이중 해쉬체인으로 묶어 다중 처리한다. 또한, 제안 기법은 계층적으로 분류된 데이터를 손쉽게 접근하기 위해서 해쉬체인으로 데이터를 구성하여 데이터의 처리 비용을 줄였다. 실험결과, 제안 기법은 기존 기법보다 데이터의 정확도는 평균 7.8% 높았고, 데이터의 처리 비용은 4.9% 단축시켰다.

Keywords

References

  1. H. Hu, Y. Wen, T. S. Chua, X. Li, "Toward Scalable Systems for Big Data Anaqlytics: A Technology Tutorial", IEEE Access, vol. 2, pp. 652-687, 2014. https://doi.org/10.1109/ACCESS.2014.2332453
  2. P. Russom, "Big Data Analytics", TDWI Research Fourth Quarter, pp. 6, Dec. 2011.
  3. V. Gadepally, J. Kepner. "Big data dimensional analysis", 2014 IEEE High Performance Extreme Computing Conference(HPEC) pp. 1-6, Sep. 2014.
  4. Y. Demchenko, C. De Laat, P. Membrey, "Defining architecture components of the Big data Ecosystem", 2014 International conference on Collaboration Technologies and Systems(CTS), pp.104-112, May, 2014.
  5. J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, A. H. Byers, "Big Data: The Next Frontier for Innovation, Competition and Productivity", Mckinsey Global Institute, pp. 1-137. 2011.
  6. P. Shen, Y. Zhou, K. Chen, "A Probability based Subnet Selection Method for Hot Event Detection in Sina Weibo Microblogging", 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1410-1413, Aug. 2013.
  7. K. Chen, Y. Zhou, H. Zha, J. He, P. Shen, X. Yang, "Cost-Effective Node Monitoring for Online Hot Event Detection in Sina Weibo", In Proceedings of the 22nd international conference on World Wide Web, ACM. pp. 107-108, April. 2013.
  8. D. Kempe, J. Klenberg, E. Tardos, "Maximizing the spread of influence through a social netowrk", In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 137-146, Aug. 2003.
  9. K. M. P. Shrivastba, M. A. Rizvi, S. Singh, "Big Data Privacy Based on Differential Privacy a Hope for Big Data", 2014 International conference on Computational Intelligence and Communication Networks, pp. 776-781. Nov. 2014.
  10. A. Katal, M. Wazid, R. H. Goudar, "Big data: Issues, challenges, tools and Good practices ", 2013 Sixth International Conference on Contemporary Computing(IC3), pp. 404-409, Aug. 2013.
  11. Y. C. Jung. "Big Data revolution and media policy issues", KISDI Premium Report, Vol. 12, No. 2, pp. 1-22, 2012.
  12. S. H. Kim, N. U. Kim, t. M. Chung, "Attribute Relationship Evaluation Methodology for Big Data Seucrity", 2013 International Conference on IT Convergence and Security(ICITCS), pp. 1-4, Dec. 2013.
  13. S. Y. Son, "Big data, online marketing and privacy protection", KISDI Premium Report, Vol. 13, No. 1, pp.1-26, 2013.
  14. J. T. Kim, B. J. Oh, J. Y. Park, "Standard Trends for the BigData Technologies", 2013 Electronics and Telecommunications Trends, Vol. 28, No. 1, pp. 92-99, 2013.
  15. M. Paryasto, A. Alamsyah, B. Rahardjo, Kuspriyanto, "Big-data security management issues", 2014 2nd International Conference on Information and Communication Technology(ICoICT), pp. 59-63, May, 2014.
  16. Laurent, E. and V.D. Gligor, "A key-management scheme for distributed sensor networks". Proceedings of the 9th ACM Conference on Computer and Communications Security, ACM Press, pp. 41-47, Nov. 2002.