Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
Introduction to Algorithms
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Indexing spatio-temporal trajectories with Chebyshev polynomials
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Context-aware optimization of continuous range queries maintenance for trajectories
Proceedings of the 4th ACM international workshop on Data engineering for wireless and mobile access
Complex spatio-temporal pattern queries
VLDB '05 Proceedings of the 31st international conference on Very large data bases
A trajectory splitting model for efficient spatio-temporal indexing
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Efficient query processing on spatial networks
Proceedings of the 13th annual ACM international workshop on Geographic information systems
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
Network Voronoi Diagram Based Range Search
AINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications
Querying trajectories using flexible patterns
Proceedings of the 13th International Conference on Extending Database Technology
Searching trajectories by locations: an efficiency study
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
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Query processing for trajectory data is very important to spatio-temporal databases. Most of the research work in this field is based on the Euclidean space. However, in many applications, the spatio-temporal trajectories of moving objects are distributed in spatial network environment intensively, and the movements of moving objects are constrained by given network segments. Under such circumstances, existing Euclidean-based methods are ineffective. In this paper, we present a Network Voronoi Diagram (NVD) based method, to solve range query problem on a huge amount of spatio-temporal trajectories in spatial networks. The experiments demonstrated that our method is very efficient even when the trajectory datasets are huge.