The BANG file: A new kind of grid file
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
Analysis of object oriented spatial access methods
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
The LSD tree: spatial access to multidimensional and non-point objects
VLDB '89 Proceedings of the 15th international conference on Very large data bases
The buddy tree: an efficient and robust access method for spatial data base
Proceedings of the sixteenth international conference on Very large databases
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
A general solution of the n-dimensional B-tree problem
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Topological relations in the world of minimum bounding rectangles: a study with R-trees
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
A study on data point search for HG-trees
ACM SIGMOD Record
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
The K-D-B-tree: a search structure for large multidimensional dynamic indexes
SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
A class of data structures for associative searching
PODS '84 Proceedings of the 3rd ACM SIGACT-SIGMOD symposium on Principles of database systems
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Tree-Based Access Methods for Spatial Databases: Implementation and Performance Evaluation
IEEE Transactions on Knowledge and Data Engineering
An Implementation and Performance Analysis of Spatial Data Access Methods
Proceedings of the Fifth International Conference on Data Engineering
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth 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
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
The Transformation Technique for Spatial Objects Revisited
SSD '93 Proceedings of the Third International Symposium on Advances in Spatial Databases
Techniques for Design and Implementation of Efficient Spatial Access Methods
VLDB '88 Proceedings of the 14th International Conference on Very Large Data Bases
Object and query transformation: supporting multi-dimensional queries through code reuse
Proceedings of the ninth international conference on Information and knowledge management
Dynamic and hierarchical spatial access method using integer searching
Proceedings of the tenth international conference on Information and knowledge management
A retrieval technique for high-dimensional data and partially specified queries
Data & Knowledge Engineering
Processing partially specified queries over high-dimensional databases
Data & Knowledge Engineering
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The development of high-performance spatial access methods that can support complex operations of large spatial databases continues to attract considerable attention. This paper introduces QSF-trees, an efficient and scalable structure for indexing spatial objects, which has some important advantages over R*-trees. QSF-trees eliminate overlapping of index regions without forcing object clipping or sacrificing the selectivity of spatial operations. The method exploits the semantics of topological relations between spatial objects to further reduce the number of index nodes visited during the search. A series of experiments involving randomly-generated spatial objects was conducted to compare the structure with two variations of R*-trees. The experiments show QSF-trees to be more efficient and more scalable to the increase in the data-set size, the size of spatial objects, and the number of dimensions of the spatial universe.