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)
Storing a collection of polygons using quadtrees
ACM Transactions on Graphics (TOG)
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
Progressive vector transmission
Proceedings of the 7th ACM international symposium on Advances in geographic information systems
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
Extensions of GAP-tree and its implementation based on a non-topological data model
International Journal of Geographical Information Science
A quantitative scale-setting approach for building multi-scale spatial databases
Computers & Geosciences
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In this paper, an efficient access method for integrating multi-scale geometric data is proposed. Previous access methods do not access multi-scale geometric data efficiently. To solve it, a few access methods for multi-scale geometric data, are known. However these methods do not support all types of multi-scale geometric data, because they support only a selection operation and a simplification operation of all map generalization operations. We propose a new method for integrating multi-scale geometric data. In the proposed method, collections of indexes in its own scale are integrated into a single index structure. By the integration, not only does the proposed method offers fast search, but also the proposed method does not introduce data redundancy. Moreover, the proposed method supports all types of multi-scale geometric data. The experimental results show that our method is an efficient method for integrating multi-scale geometric data.