SIAM Journal on Computing
Shortest paths on a polyhedron
SCG '90 Proceedings of the sixth annual symposium on Computational geometry
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)
Influence sets based on reverse nearest neighbor queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Discovery of Influence Sets in Frequently Updated Databases
Proceedings of the 27th International Conference on Very Large Data Bases
STR: A simple and efficient algorithm for R-tree packing
STR: A simple and efficient algorithm for R-tree packing
On computing top-t most influential spatial sites
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Reverse Nearest Neighbors in Large Graphs
IEEE Transactions on Knowledge and Data Engineering
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
The TPR*-tree: an optimized spatio-temporal access method for predictive queries
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Reverse kNN search in arbitrary dimensionality
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
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
FINCH: evaluating reverse k-Nearest-Neighbor queries on location data
Proceedings of the VLDB Endowment
Continuous monitoring of nearest neighbors on land surface
Proceedings of the VLDB Endowment
Scalable shortest paths browsing on land surface
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Spatial Network RNN Queries in GIS
The Computer Journal
Finding shortest path on land surface
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Influence zone: Efficiently processing reverse k nearest neighbors queries
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Continuous reverse k nearest neighbors queries in Euclidean space and in spatial networks
The VLDB Journal — The International Journal on Very Large Data Bases
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Finding reverse nearest neighbors (RNNs) is an important operation in spatial databases. The problem of evaluating RNN queries has already received considerable attention due to its importance in many real-world applications, such as resource allocation and disaster response. While RNN query processing has been extensively studied in Euclidean space, no work ever studies this problem on land surfaces. However, practical applications of RNN queries involve terrain surfaces that constrain object movements, which rendering the existing algorithms inapplicable. In this paper, we investigate the evaluation of two types of RNN queries on land surfaces: monochromatic RNN (MRNN) queries and bichromatic RNN (BRNN) queries. On a land surface, the distance between two points is calculated as the length of the shortest path along the surface. However, the computational cost of the state-of-the-art shortest path algorithm on a land surface is quadratic to the size of the surface model, which is usually quite huge. As a result, surface RNN query processing is a challenging problem. Leveraging some newly-discovered properties of Voronoi cell approximation structures, we make use of standard index structures such as an R-tree to design efficient algorithms that accelerate the evaluation of MRNN and BRNN queries on land surfaces. Our proposed algorithms are able to localize query evaluation by accessing just a small fraction of the surface data near the query point, which helps avoid shortest path evaluation on a large surface. Extensive experiments are conducted on large real-world datasets to demonstrate the efficiency of our algorithms.