NDLI: Local network Voronoi diagrams

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Voronoi-based aggregate nearest neighbor query processing in road networks

Local network Voronoi diagrams

Content Provider	ACM Digital Library
Author	Nutanong, Sarana Ali, Mohammed Eunus Tanin, Egemen Kulik, Lars
Abstract	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.
Starting Page	109
Ending Page	118
Page Count	10
File Format	PDF
ISBN	9781450304283
DOI	10.1145/1869790.1869808
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-11-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Query processing Nearest neighbor Spatial databases
Content Type	Text
Resource Type	Article

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