Earticle

초록

영어

In many applications such as sensor networks, similarity search is more practical than exact match in stream processing, where both the queries and the data items are always change over time. The volumes of multi-streams could be very large, since new items are continuously appended. The main idea is to build a small size of synopsis instead of keeping original streams by using our proposed techniques, then to provide approximate answers for many different classes of aggregate queries. In this paper, we present D-skyline and T-skyline methods give almost “true” results on approximated analysis for similarity search query in streaming environments.

목차

Abstract
 1. Introduction
 2. Skyline Operator
  2.1. D-Skyline Technique
  2.2. T-Skyline Technique
 3. Experiment and Evaluation
 4. Summary
 Acknowledgements
 References

키워드

저자

• Ling Wang [ Department of Computer Science and Technology, School of Information Engineering, Northeast Dianli University, Jilin, China ]
• Tie Hua Zhou [ Database/Bioinformatics Laboratory, School of Electrical & Computer Engineering, Chungbuk National University, Chungbuk, Korea ]
• Kyung Ah Kim [ Department of Biomedical Engineering, Chungbuk National University, Chungbuk, Korea ]
• Eun Jong Cha [ Department of Biomedical Engineering, Chungbuk National University, Chungbuk, Korea ]
• Keun Ho Ryu [ Database/Bioinformatics Laboratory, School of Electrical & Computer Engineering, Chungbuk National University, Chungbuk, Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605