A multi-resolution surface distance model for k-NN query processing
The VLDB Journal — The International Journal on Very Large Data Bases
Indexing land surface for efficient kNN query
Proceedings of the VLDB Endowment
Continuous visible nearest neighbor queries
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
On efficient mutual nearest neighbor query processing in spatial databases
Data & Knowledge Engineering
Continuous monitoring of nearest neighbors on land surface
Proceedings of the VLDB Endowment
Best point detour query in road networks
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Finding shortest path on land surface
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Continuous visible nearest neighbor query processing in spatial databases
The VLDB Journal — The International Journal on Very Large Data Bases
Monochromatic and bichromatic reverse nearest neighbor queries on land surfaces
Proceedings of the 21st ACM international conference on Information and knowledge management
Data centric research at the University of Queensland
ACM SIGMOD Record
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A k-NN query finds the k nearest-neighbors of a given point from a point database. When it is sufficient to measure object distance using the Euclidian distance, the key to efficient k-NN query processing is to fetch and check the distances of a minimum number of points from the database. For many applications, such as vehicle movement along road networks or rover and animal movement along terrain surfaces, the distance is only meaningful when it is along a valid movement path. For this type of k-NN queries, the focus of efficient query processing is to minimize the cost of computing distances using the environment data (such as the road network data and the terrain data), which can be several orders of magnitude larger than that of the point data. Efficient processing of k-NN queries based on the Euclidian distance or the road network distance has been investigated extensively in the past. In this paper, we investigate the problem of surface k-NN query processing, where the distance is calculated from the shortest path along a terrain surface. This problem is very challenging, as the terrain data can be very large and the computational cost of finding shortest paths is very high. We propose an efficient solution based on multiresolution terrain models. Our approach eliminates the need of costly process of finding shortest paths by ranking objects using estimated lower and upper bounds of distance on multiresolution terrain models.