SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
Novel Approaches in Query Processing for Moving Object Trajectories
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Nearest Neighbor and Reverse Nearest Neighbor Queries for Moving Objects
IDEAS '02 Proceedings of the 2002 International Symposium on Database Engineering & Applications
On the Generation of Spatiotemporal Datasets
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
K-Nearest Neighbor Search for Moving Query Point
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Constrained Nearest Neighbor Queries
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Continuous nearest neighbor search
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Efficient k-nearest-neighbor search algorthims for historical moving object trajectories
Journal of Computer Science and Technology
Efficient algorithms for historical continuous kNN query processing over moving object trajectories
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Efficient algorithms to monitor continuous constrained k nearest neighbor queries
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Mobile Information Systems
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Given a set D of trajectories, a query object (point or trajectory) q, a time interval T, and a constrained region CR, a constrained k-nearest neighbor (CkNN) query over moving object trajectories retrieves from D within T, the k (≥ 1) trajectories that lie closest to q and intersect (or are enclosed by) CR. In this paper, we propose several algorithms for efficiently processing CkNN search on moving object trajectories. In particular, we thoroughly investigate two types of CkNN queries, viz. CkNNP and CkNNT queries, which are defined w.r.t. stationary query points and moving query trajectories, respectively. The performance of our algorithms is evaluated with extensive experiments using both real and synthetic datasets.