The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
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)
Closest pair queries in spatial databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Time-parameterized queries in spatio-temporal databases
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
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
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
A Threshold-Based Algorithm for Continuous Monitoring of k Nearest Neighbors
IEEE Transactions on Knowledge and Data Engineering
R-Trees: Theory and Applications (Advanced Information and Knowledge Processing)
R-Trees: Theory and Applications (Advanced Information and Knowledge Processing)
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
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
Spatio-Temporal Indexing for Large Multimedia Applications
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
Nearest neighbor search on moving object trajectories
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Efficient mutual nearest neighbor query processing for moving object trajectories
Information Sciences: an International Journal
Constrained k-nearest neighbor query processing over moving object trajectories
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
Efficient k-nearest neighbor search on moving object trajectories
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient identification and approximation of k-nearest moving neighbors
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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In this paper, we investigate the problem of efficiently processing historical continuous k-Nearest Neighbor (HCkNN) queries on R-treelike structures storing historical information about moving object trajectories. The existing approaches for HCkNN queries need high I/O (i.e., number of node accesses) and CPU costs since they follow depth-first fashion. Motivated by this observation, we present two algorithms, called HCP-kNN and HCT-kNN, which deal with the HCkNN retrieval with respect to the stationary query point and the moving query trajectory, respectively. The core of our solution employs best-first traversal paradigm and enables effective update strategies to maintain the nearest lists. Extensive performance studies with real and synthetic datasets show that the proposed algorithms outperform their competitors significantly in both efficiency and scalability.