Merged aggregate nearest neighbor query processing in road networks

Authors:
Weiwei Sun;Chong Chen;Baihua Zheng;Chunan Chen;Liang Zhu;Weimo Liu;Yan Huang
Affiliations:
Fudan University, Shanghai, China;Fudan University, Shanghai, China;Singapore Management University, Singapore City, Singapore;Fudan University, Shanghai, China;Fudan University, Shanghai, China;Fudan University, Shanghai, China;University of North Texas, Denton, USA
Venue:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Year:
2013

Citing 10
Cited 0

Distance browsing in spatial databases

ACM Transactions on Database Systems (TODS)
Group Nearest Neighbor Queries

ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Aggregate Nearest Neighbor Queries in Road Networks

IEEE Transactions on Knowledge and Data Engineering
Distance indexing on road networks

VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Query processing in spatial network databases

VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Voronoi-based K nearest neighbor search for spatial network databases

VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Proximity queries in large traffic networks

Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Scalable network distance browsing in spatial databases

Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Voronoi-based aggregate nearest neighbor query processing in road networks

Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
ROAD: A New Spatial Object Search Framework for Road Networks

IEEE Transactions on Knowledge and Data Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

Aggregate nearest neighbor query, which returns a common interesting point that minimizes the aggregate distance for a given query point set, is one of the most important operations in spatial databases and their application domains. This paper addresses the problem of finding the aggregate nearest neighbor for a merged set that consists of the given query point set and multiple points needed to be selected from a candidate set, which we name as merged aggregate nearest neighbor(MANN) query. This paper proposes an effective algorithm to process MANN query in road networks based on our pruning strategies. Extensive experiments are conducted to examine the behaviors of the solutions and the overall experiments show that our strategies to minimize the response time are effective and achieve several orders of magnitude speedup compared with the baseline methods.