Time-parameterized queries in spatio-temporal databases
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
K-Nearest Neighbor Search for Moving Query Point
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Monitoring k-Nearest Neighbor Queries over Moving Objects
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
SEA-CNN: Scalable Processing of Continuous K-Nearest Neighbor Queries in Spatio-temporal Databases
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Continuous nearest neighbor search
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Continuous K-nearest neighbor queries for continuously moving points with updates
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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Spatio-temporal databases aim at combining the spatial and temporal characteristics of data. The continuous K-Nearest Neighbor (CKNN) query is an important type of spatio-temporal query that finds the K-Nearest Neighbors (KNNs) of a moving query object at each time instant within a given time interval [ts, te]. In this paper, we investigate how to process a CKNN query efficiently under the situation that each object moves with an uncertain velocity. This uncertainty on the velocity of each object inevitably results in high complexity of the CKNN problem. We propose a cost-effective PKNN algorithm to tackle the complicated problem incurred by this uncertainty.