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
Spatial priority search: an access technique for scaleless maps
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
The Quadtree and Related Hierarchical Data Structures
ACM Computing Surveys (CSUR)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
On multi-scale display of geometric objects
Data & Knowledge Engineering
The R-File: An Efficient Access Structure for Proximity Queries
Proceedings of the Sixth International Conference on Data Engineering
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Hilbert R-tree: An Improved R-tree using Fractals
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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An efficient access technique is one of the most important requirements in web-based GIS. With levelized spatial data we can access spatial data efficiently, because of no access to the fully detailed spatial data. Previous spatial access methods have disadvantages, such as data redundancy and low search performance, for levelized spatial data. To solve it, a few spatial access methods for levelized spatial data, are proposed. However these methods support only a few kinds of levelized data, i.e, data through a selection operation and a simplification operation. For the effects, we propose a new spatial access method supporting all kinds of levelized spatial data. A new access structure is designed and implemented. Experiments are then performed on real data sets. The results show that our method offers both high search performance and no data redundancy.