Spatial query processing in an object-oriented database system
SIGMOD '86 Proceedings of the 1986 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
Equi-depth multidimensional histograms
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Applications of spatial data structures: Computer graphics, image processing, and GIS
Applications of spatial data structures: Computer graphics, image processing, and GIS
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 comparison of spatial query processing techniques for native and parameter spaces
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
On the propagation of errors in the size of join results
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Analytical results on the quadtree decomposition of arbitrary rectangles
Pattern Recognition Letters
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
Optimal histograms for limiting worst-case error propagation in the size of join results
ACM Transactions on Database Systems (TODS)
The SEQUOIA 2000 storage benchmark
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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
Efficient and effective querying by image content
Journal of Intelligent Information Systems - Special issue: advances in visual information management systems
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Implications of certain assumptions in database performance evauation
ACM Transactions on Database Systems (TODS)
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Performance of Nearest Neighbor Queries in R-Trees
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Optimization for Spatial Query Processing
VLDB '91 Proceedings of the 17th International Conference on Very Large Data Bases
On B-Tree Indices for Skewed Distributions
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
VLDB '94 Proceedings of the 20th 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
GESS: a scalable similarity-join algorithm for mining large data sets in high dimensional spaces
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of predictive spatio-temporal queries
ACM Transactions on Database Systems (TODS)
Approximation techniques for spatial data
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Cost models for distance joins queries using R-trees
Data & Knowledge Engineering
Evaluating the intrinsic dimension of evolving data streams
Proceedings of the 2006 ACM symposium on Applied computing
A fast and effective method to find correlations among attributes in databases
Data Mining and Knowledge Discovery
REDUS: finding reducible subspaces in high dimensional data
Proceedings of the 17th ACM conference on Information and knowledge management
Measuring evolving data streams' behavior through their intrinsic dimension
New Generation Computing
Time-Aware Similarity Search: A Metric-Temporal Representation for Complex Data
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
A generic framework for handling uncertain data with local correlations
Proceedings of the VLDB Endowment
Probabilistic time consistent queries over moving objects
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Nearest neighbor search on vertically partitioned high-dimensional data
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Overlaying multiple maps efficiently
CIT'04 Proceedings of the 7th international conference on Intelligent Information Technology
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The problem of selectivity estimation for queries of nontraditional databases is still an open issue. In this article, we examine the problem of selectivity estimation for some types of spatial queries in databases containing real data. We have shown earlier [Faloutsos and Kamel 1994] that real point sets typically have a nonuniform distribution, violating consistently the uniformity and independence assumptions. Moreover, we demonstrated that the theory of fractals can help to describe real point sets. In this article we show how the concept of fractal dimension, i.e., (noninteger) dimension, can lead to the solution for the selectivity estimation problem in spatial databases. Among the infinite family of fractal dimensions, we consider here the Hausdorff fractal dimension D0 and the “Correlation” fractal dimension D2. Specifically, we show that (a) the average number of neighbors for a given point set follows a power law, with D2 as exponent, and (b) the average number of nonempty range queries follows a power law with E − D0 as exponent (E is the dimension of the embedding space). We present the formulas to estimate the selectivity for “biased” range queries, for self-spatial joins, and for the average number of nonempty range queries. The result of some experiments on real and synthetic point sets are shown. Our formulas achieve very low relative errors, typically about 10%, versus 40%–100% of the formulas that are based on the uniformity and independence assumptions.