Earticle

초록

영어

The paper proposes spatial approximate keyword query algorithms for cloud systems. Existing work targets on single server solutions, and an exact algorithm is given in memory while another approximate algorithm is given for disk resident datasets. However, a single server fails to provide reasonable throughput due to the limited CPU time and disk bandwidth. Facing the above challenges, this paper gives a two-layered index consisting of global index and local index, which works in a shared nothing cluster for larger query throughput. This paper designs a novel external memory index as local index, which returns exact answer within disks efficiently. It is equipped with keyword set signature and multiple optimizing strategies to reduce I/O cost. The global index partitions the entire spatial space, and each computing node in system maintains a partition. A global index selection algorithm is given. This paper also provides spatial approximate keyword query algorithms based edit distance, including range and the nearest neighbor spatial conditions. An experiment in a shared nothing cluster illustrates the efficiency and effectiveness of our proposed index and query algorithms.

목차

Abstract
 1. Introduction
 2. System Framework
 3. External Index RB Tree
  3.1. The Structure of RB Tree
  3.2. RB Tree Query Processing
  3.3. RBF Index Query Processing
 4. Experiment Design and Discussion
  4.1. The Experimental Setup
  4.2 The Performance of Local RB Tree
  4.3. Global Query Performance
 5. Conclusion
 References

키워드

저자

• Zuping Liu [ Sichuan Vocational and Technical College, Suining 629000, 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