Redundancy in spatial databases
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
The design and analysis of spatial data structures
The design and analysis of spatial data structures
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
Multi-step processing of spatial joins
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases
Proceedings of the Fifth International Conference on Data Engineering
Proceedings of the Ninth International Conference on Data Engineering
Approximations for a Multi-Step Processing of Spatial Joins
IGIS '94 Proceedings of the International Workshop on Advanced Information Systems: Geographic Information Systems
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
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
Properties of Geographic Data: Requirements for Spatial Access Methods
SSD '91 Proceedings of the Second International Symposium on Advances in Spatial Databases
Proceedings of the Sixth International Conference on Data Engineering
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Spatial query processing using a Spatial Access Method (SAM) faces the problem of having to examine a large number of candidate objects during the CPU-time intensive refinement step. This is due to the Minimum Bounding Rectangle (MBR) filter in the first step of query processing which is rough by nature. In order to overcome these problem, the multi-step filtering method that takes a series of spatial filters with higher filtering ratios than that of the MBR in a cascade fashion for the object set already filtered by an MBR has been introduced. Most of the spatial filters were only able to manage areal objects. In this paper, we propose the Minimum Maximum Points (MMP) filter, a spatial filter that can manage not only areal objects but also linear objects. In addition, we propose a multi-step filtering processor using the MMP filter, which is designed for well-known spatial operator respectively. We also show the superiority of our multi-step filtering by extensive experiments.