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
Continuous probabilistic nearest-neighbor queries for uncertain trajectories
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Continuous K-Nearest Neighbor Query over Moving Objects in Road Networks
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
The VLDB Journal — The International Journal on Very Large Data Bases
The islands approach to nearest neighbor querying in spatial networks
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Ranking continuous nearest neighbors for uncertain trajectories
The VLDB Journal — The International Journal on Very Large Data Bases
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This paper focuses on processing continuous k nearest neighbor queries over objects moving at uncertain speeds (CUkNN) in road networks. We present a novel model to estimate the distances between objects and a query, both of which move at variable speeds in the road network. Based on the proposed distance model, we present a CUkNN query monitoring method to continuously find the objects that could potentially be the k-nearest neighbors (kNN) of the query. We propose an efficient method to calculate the probability of each object being a kNN of a query. The key thing about the method is that the probability of an object being a kNN of query q is shown to be equivalent to the probability of a special line segment being one of the k-nearest lines from q, which greatly simplifies the probability calculation.