Equi-depth multidimensional histograms
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Practical selectivity estimation through adaptive sampling
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Sequential sampling procedures for query size estimation
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
Wavelet-based histograms for selectivity estimation
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data cube approximation and histograms via wavelets
Proceedings of the seventh international conference on Information and knowledge management
Selectivity estimation in spatial databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Self-tuning histograms: building histograms without looking at data
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Multi-dimensional selectivity estimation using compressed histogram information
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
A comparison of selectivity estimators for range queries on metric attributes
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
STHoles: a multidimensional workload-aware histogram
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Clustering Algorithms
Dynamic multidimensional histograms
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Fast incremental maintenance of approximate histograms
ACM Transactions on Database Systems (TODS)
Accurate estimation of the number of tuples satisfying a condition
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
On Rectangular Partitionings in Two Dimensions: Algorithms, Complexity, and Applications
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Optimal Histograms with Quality Guarantees
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Selectivity Estimation Without the Attribute Value Independence Assumption
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
The optimization of queries in relational databases
The optimization of queries in relational databases
Selectivity estimators for multidimensional range queries over real attributes
The VLDB Journal — The International Journal on Very Large Data Bases
ISOMER: Consistent Histogram Construction Using Query Feedback
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
The Effectiveness of Lloyd-Type Methods for the k-Means Problem
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
The history of histograms (abridged)
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
REHIST: relative error histogram construction algorithms
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Rk-hist: an r-tree based histogram for multi-dimensional selectivity estimation
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Hierarchically organized skew-tolerant histograms for geographic data objects
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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Histograms have been widely used for estimating selectivity in query optimization. In this paper, we propose a new histogram construction method for geographic data objects that are used in many real-world applications. The proposed method is based on analyses and utilization of clusters of objects that exist in a given data set, to build histograms with significantly enhanced accuracy. Our philosophy in allocating the histogram buckets is to allocate them to the subspaces that properly capture object clusters. Therefore, we first propose a procedure to find the centers of object clusters. Then, we propose an algorithm to construct the histogram buckets from these centers. The buckets are initialized from the clusters' centers, then expanded to cover the clusters. Best expansion plans are chosen based on a notion of skewness gain. Results from extensive experiments using real-life data sets demonstrate that the proposed method can really improve the accuracy of the histograms further, when compared with the current state of the art histogram construction method for geographic data objects.