Multiattribute hashing using Gray codes
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
Spatial query processing in an object-oriented database system
SIGMOD '86 Proceedings of the 1986 ACM SIGMOD international conference on Management of data
Gray Codes for Partial Match and Range Queries
IEEE Transactions on Software Engineering
Fractals for secondary key retrieval
PODS '89 Proceedings of the eighth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Linear clustering of objects with multiple attributes
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Analysis of the Hilbert curve for representing two-dimensional space
Information Processing Letters
Closest pair queries in spatial databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Performance of multi-dimensional space-filling curves
Proceedings of the 10th ACM international symposium on Advances in geographic information systems
A class of data structures for associative searching
PODS '84 Proceedings of the 3rd ACM SIGACT-SIGMOD symposium on Principles of database systems
An introduction to spatial database systems
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Analysis of the Clustering Properties of the Hilbert Space-Filling Curve
IEEE Transactions on Knowledge and Data Engineering
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
Combinatorial Algorithms: Theory and Practice
Combinatorial Algorithms: Theory and Practice
Information Sciences: an International Journal
Mapping with Space Filling Surfaces
IEEE Transactions on Parallel and Distributed Systems
Sensing and acting with predefined trajectories
Proceedings of the 1st ACM international workshop on Heterogeneous sensor and actor networks
All-nearest-neighbors finding based on the Hilbert curve
Expert Systems with Applications: An International Journal
Algorithm for analyzing n-dimensional hilbert curve
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
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Nearest-neighbor-finding is one of the most important spatial operations in the field of spatial data structures concerned with proximity. Because the goal of the space-filling curves is to preserve the spatial proximity, the nearest neighbor queries can be handled by these space-filling curves. When data are ordered by the Peano curve, we can directly compute the sequence numbers of the neighboring blocks next to the query block in eight directions in the 2D- space based on its bit shuffling property. But when data are ordered by the RBG curve or the Hilbert curve, neighbor-finding is complex. However, we observe that there is some relationship between the RBG curve and the Peano curve, as with the Hilbert curve. Therefore, in this paper, we first show the strategy based on the Peano curve for the nearest-neighbor query. Next, we present the rules for transformation between the Peano curve and the other two curves, including the RBG curve and the Hilbert curve, such that we can also efficiently find the nearest neighbor by the strategies based on these two curves. From our simulation, we show that the strategy based on the Hilbert curve requires the least total time (the CPU-time and the I/O time) to process the nearest-neighbor query among our three strategies, since it can provide the good clustering property.