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
Linear clustering of objects with multiple attributes
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
Encoding and decoding the Hilbert order
Software—Practice & Experience
Analysis of the Hilbert curve for representing two-dimensional space
Information Processing Letters
A strategy for repetitive neighbor finding in images represented by quadtrees
Pattern Recognition Letters
Indexing support for spatial joins
Data & Knowledge Engineering
Vertex-labeling algorithms for the Hilbert spacefilling curve
Software—Practice & Experience
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Analysis of the Clustering Properties of the Hilbert Space-Filling Curve
IEEE Transactions on Knowledge and Data Engineering
Performance of Nearest Neighbor Queries in R-Trees
ICDT '97 Proceedings of the 6th International Conference on Database Theory
High Dimensional Similarity Search With Space Filling Curves
Proceedings of the 17th International Conference on Data Engineering
The Buddy-Tree: An Efficient and Robust Access Method for Spatial Data Base Systems
VLDB '90 Proceedings of the 16th International Conference on Very Large Data Bases
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
Location-based spatial queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Analysis of Multi-Dimensional Space-Filling Curves
Geoinformatica
All-Nearest-Neighbors Queries in Spatial Databases
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Object-based and image-based object representations
ACM Computing Surveys (CSUR)
Neighbor-finding based on space-filling curves
Information Systems
Spatial ordering and encoding for geographic data mining and visualization
Journal of Intelligent Information Systems
Information Sciences: an International Journal
A new algorithm for encoding and decoding the Hilbert order
Software—Practice & Experience
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Localization and coverage for high density sensor networks
Computer Communications
The Bdual-Tree: indexing moving objects by space filling curves in the dual space
The VLDB Journal — The International Journal on Very Large Data Bases
Hi-index | 12.06 |
An all-nearest-neighbors (ANN) query retrieves all nearest neighbors to all query objects. We may perform large number of one-nearest-neighbor queries to answer such an ANN query. Due to no total ordering of spatial proximity among spatial objects, the Hilbert curve approach has proposed to preserve the spatial locality. Chen and Chang have proposed a neighbor finding strategy (denoted as the CCSF strategy) based on the Hilbert curve to compute the absolute location of the neighboring blocks. However, it costs much time during the transformation between the Hilbert curve and the Peano curve. On the other hand, in the strategy based on R or R^*-trees for an ANN query, large number of unnecessary distance comparisons have to be done due to the problem of overlaps within the R-tree, resulting in many redundant disk accesses. Therefore, in this paper, we first propose the one-nearest-neighbor finding strategy directly based on the Hilbert curve (denoted as the ONHC strategy) for a one-nearest-neighbor query. By relations among orientations, orders, and quaternary numbers, we compute the relative locations of the query block and the neighboring block in the Hilbert curve. Then, the nearest neighbor of one query point can be found directly from these neighboring blocks. Next, by using our ONHC strategy, we propose the all-nearest-neighbors finding strategy based on the Hilbert curve (denoted as the ANHC strategy) for an ANN query. Finally, from the simulation result, we show that our ONHC strategy needs less response time (the CPU-time and the I/O time) than the CCSF strategy for the one-nearest-neighbor query. We also show that our ANHC strategy needs less response time than the strategy based on R^*-trees for an ANN query.