Group visible nearest neighbor queries in spatial databases

Xu, Hu, Li, Zhicheng, Lu, Yansheng, Deng, Ked and Zhou, Xiaofang (2010). Group visible nearest neighbor queries in spatial databases. In: Lei Chen, Changjie Tang, Jun Yang and Yunjun Gao, Web-Age Information Management - 11th International Conference, WAIM 2010, Proceedings. 11th International Conference on Web-Age Information Management, WAIM 2010, Jiuzhaigou, (333-344). July 15, 2010-July 17, 2010. doi:10.1007/978-3-642-14246-8_33


Author Xu, Hu
Li, Zhicheng
Lu, Yansheng
Deng, Ked
Zhou, Xiaofang
Title of paper Group visible nearest neighbor queries in spatial databases
Conference name 11th International Conference on Web-Age Information Management, WAIM 2010
Conference location Jiuzhaigou
Conference dates July 15, 2010-July 17, 2010
Proceedings title Web-Age Information Management - 11th International Conference, WAIM 2010, Proceedings   Check publisher's open access policy
Journal name Lecture Notes in Computer Science   Check publisher's open access policy
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2010
Year available 2010
Sub-type Fully published paper
DOI 10.1007/978-3-642-14246-8_33
Open Access Status
ISBN 3642142451
ISSN 0302-9743
1611-3349
Editor Lei Chen
Changjie Tang
Jun Yang
Yunjun Gao
Volume 6184
Start page 333
End page 344
Total pages 12
Collection year 2011
Language eng
Abstract/Summary Traditional nearest neighbor queries and its variants, such as Group Nearest Neighbor Query (GNN), have been widely studied by many researchers. Recently obstacles are involved in spatial queries. The existence of obstacles may affect the query results due to the visibility of query point. In this paper, we propose a new type of query, Group Visible Nearest Neighbor Query (GVNN), which considers both visibility and distance as constraints. Multiple Traversing Obstacles (MTO) Algorithm and Traversing Obstacles Once (TOO) Algorithm are proposed to efficiently solve GVNN problem. TOO resolves GVNN by defining the invisible region of MBR of query set to prune both data set and obstacle set, and traverses obstacle R*-tree only once. The experiments with different settings show that TOO is more efficient and scalable than MTO.
Subjects 1700 Computer Science
2614 Theoretical Computer Science
Keyword Group nearest neighbor
Query processing
Spatial database
Visible nearest neighbor
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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