Earticle

초록

영어

In order to deal with multiple user preferences and improve query efficiency, selection strategy is adopted for top-k query to depress the compare operations. Firstly, the kth order statistics are selected randomly along with partitioning the data set basing on it, and the top-k result set can be received after several recursive partitions. Secondly, to select the kth order statistics accurately, the approximate kth order statistics is choose as threshold according to the similarity of user preference and system preference, and the top-k query result set can be accessed through simple comparison. Finally, the time complexities of presented algorithms are analyzed and their correctness and completeness are proved respectively. The experimental results show that our algorithms improve the efficiency of top-k query greatly.

목차

Abstract
 1. Introduction
 2. Definition
 3. Top-K Algorithm Based on Random
 4. Top-K Algorithm Based on Approximate Selection
 5. The Correctness and Completeness of Algorithms
 6. Experimental
  6.1 Experimental Preparation
  6.2 Results and Discussion
 7. Conclusion
 Acknowledgement
 References

키워드

저자

• Song Jin-ling [ Hebei Normal University of Science & Technology, Qinhuangdao 066004, China ]
• Liu Guo-hua [ College of Computer Science and Technology, Donghua University, Shanghai 201620, China ]
• Liu Hai-bin [ Hebei Normal University of Science & Technology, Qinhuangdao 066004, China ]
• Huang Li-ming [ Hebei Normal University of Science & Technology, Qinhuangdao 066004, China ]
• Wu Yun-long [ College of Computer Science and Technology, Donghua University, Shanghai 201620, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605