Earticle

초록

영어

Actually, large database is not simply considered as a stream database because of streaming data is not only containing huge data volumes, but distributed, continuous, rapid, time varying. Therefore, the general techniques may not suit for streams exactly. Accuracy responses required of approximated answers is more important in stream processing for the similarity search. Therefore, we perform data reduction across synopsis data structure and to batch processing in a particular relevance way on the data stream computation model over sliding windows. Focus on similarity search in streaming environment, D-skyline method proposed in this paper concern useful aggregate as a preprocessing phase instead of original dataset repeatedly processing manner, in order to efficiently optimize both in space usage and error control. Our experimental evaluation would show the detailed effect on approximated analysis by using different kinds of skyline methods, then effectiveness and efficiency of our approach.

목차

Abstract
 1. Introduction
 2. Skyline Operator
  2.1. D-skyline Method
  2.2. D-skyline Algorithm
 3. Experiment and Evaluation
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Ling Wang [ Database/Bioinformatics Laboratory, School of Electrical & Computer Engineering, Chungbuk National University ]
• Tie Hua Zhou [ Database/Bioinformatics Laboratory, School of Electrical & Computer Engineering, Chungbuk National University ]
• Kyung Ah Kim [ Department of Biomedical Engineering, Chungbuk National University ]
• Eun Jong Cha [ Department of Biomedical Engineering, Chungbuk National University ]
• Keun Ho Ryu [ Database/Bioinformatics Laboratory, School of Electrical & Computer Engineering, Chungbuk National University ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605