Fibonacci heaps and their uses in improved network optimization algorithms
Journal of the ACM (JACM)
Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
An efficient and scalable approach to CNN queries in a road network
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Continuous nearest neighbor monitoring in 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
Scalable network distance browsing in spatial databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Generalized network Voronoi diagrams: Concepts, computational methods, and applications
International Journal of Geographical Information Science
Instance optimal query processing in spatial networks
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient Continuous Nearest Neighbor Query in Spatial Networks Using Euclidean Restriction
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Indexing network voronoi diagrams
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
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Continuous queries in road networks have gained significant research interests due to advances in GIS and mobile computing. Consider the following scenario: "A driver uses a networked GPS navigator to monitor five nearest gas stations in a road network." The main challenge of processing such a moving query is how to efficiently monitor network distances of the k nearest and possible resultant objects. To enable result monitoring in real-time, researchers have devised techniques which utilize precomputed distances and results, e.g., the network Voronoi diagram (NVD). However, the main drawback of preprocessing is that it requires access to all data objects and network nodes, which means that it is not suitable for large datasets in many real life situations. The best existing method to monitor kNN results without precomputation relies on executions of snapshot queries at network nodes encountered by the query point. This method results in repetitive distance evaluation over the same or similar sets of nodes. In this paper, we propose a method called the local network Voronoi diagram (LNVD) to compute query answers for a small area around the query point. As a result, our method requires neither precomputation nor distance evaluation at every intersection. According to our extensive analysis and experimental results, our method significantly outperforms the best existing method in terms of data access and computation costs.