An algorithm for displaying a class of space-filling curves
Software—Practice & Experience
Analysis of object oriented spatial access methods
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
Equi-depth multidimensional histograms
SIGMOD '88 Proceedings of the 1988 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 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
Linear clustering of objects with multiple attributes
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Towards an analysis of range query performance in spatial data structures
PODS '93 Proceedings of the twelfth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Beyond uniformity and independence: analysis of R-trees using the concept of fractal dimension
PODS '94 Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Modern database systems
An advancing front Delaunay triangulation algorithm designed for robustness
Journal of Computational Physics
A model for the prediction of R-tree performance
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Histogram-based estimation techniques in database systems
Histogram-based estimation techniques in database systems
Selectivity estimation in spatial databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Direct spatial search on pictorial databases using packed R-trees
SIGMOD '85 Proceedings of the 1985 ACM SIGMOD international conference on Management of data
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
Storing spatial data on a network of workstations
Cluster Computing
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Accurate estimation of the number of tuples satisfying a condition
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Spatial Databases-Accomplishments and Research Needs
IEEE Transactions on Knowledge and Data Engineering
The Effect of Buffering on the Performance of R-Trees
ICDE '98 Proceedings of the Fourteenth 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
Optimization for Spatial Query Processing
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
Estimating the Selectivity of Spatial Queries Using the `Correlation' Fractal Dimension
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
I/O Complexity for Range Queries on Region Data Stored Using an R-tree
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Analyzing Range Queries on Spatial Data
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Proceedings of the 20th Workshop on Principles of Advanced and Distributed Simulation
Workload-based generation of administrator hints for optimizing database storage utilization
ACM Transactions on Storage (TOS)
Data access in distributed simulations of multi-agent systems
Journal of Systems and Software
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Analysis of range queries on spatial (multidimensional) data is both important and challenging. Most previous analysis attempts have made certain simplifying assumptions about the data sets and/or queries to keep the analysis tractable. As a result, they may not be universally applicable. This paper proposes a set of five analysis techniques to estimate the selectivity and number of index nodes accessed in serving a range query. The underlying philosophy behind these techniques is to maintain an auxiliary data structure, called a density file, whose creation is a one-time cost, which can be quickly consulted when the query is given. The schemes differ in what information is kept in the density file, how it is maintained, and how this information is looked up. It is shown that one of the proposed schemes, called Cumulative Density (CD), gives very accurate results (usually less than 5 percent error) using a diverse suite of point and rectangular data sets, that are uniform or skewed, and a wide range of query window parameters. The estimation takes a constant amount of time, which is typically lower than 1 percent of the time that it would take to execute the query, regardless of data set or query window parameters.