Earticle

초록

영어

As an important decision support query, Group Nearest Neighbor (GNN) query has received considerable attention from Location Based Service (LBS) research community. Previous works paid much attention to the uncertain data objects (P). Nevertheless, very little work has done to the scenario when query objects (Q) are also uncertain. In this paper, The Range-based Probabilistic Group Nearest Neighbor (in short RP-GNN) query is introduced to draw a comprehensive discussion for this extended scenario. Two novel pruning methods are proposed to improve the performance of RP-GNN. The effectiveness, efficiency and scalability of proposed methods are validated through extensive experiments. The proposed methods achieve an average speed-up of 62.2% against existing probabilistic GNN algorithms and 1-2 orders of magnitude against linear scan.

목차

Abstract
 1. Introduction
 2. Related Works
  2.1. Group Nearest Neighbor Query
  2.2. Range based Query
  2.3. Proposed Architecture
 3. Problem Definition
  4.1. Query Objects Pruning
  4.2. Geometric Pruning
  4.3. Algorithm
 5. Performance Evaluation
  5.1. Experiment Settings
  5.2. Experiment Results
 6. Conclusions
 Acknowledgments
 References

키워드

저자

• Peng Chen [ Department of Computer Science and Technology, East China Normal University ]
• Junzhong Gu [ Department of Computer Science and Technology, East China Normal University ]
• Xin Lin [ Department of Computer Science and Technology, East China Normal University ]
• Rong Tan [ Department of Computer Science and Technology, East China Normal University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605