SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Adaptive selectivity estimation using query feedback
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Selectivity and cost estimation for joins based on random sampling
Journal of Computer and System Sciences
Histogram-based estimation techniques in database systems
Histogram-based estimation techniques in database systems
An overview of query optimization in relational systems
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Efficient mid-query re-optimization of sub-optimal query execution plans
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
New sampling-based summary statistics for improving approximate query answers
SIGMOD '98 Proceedings of the 1998 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
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
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Join synopses for approximate query answering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Synopsis data structures for massive data sets
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Global optimization of histograms
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Optimal Histograms with Quality Guarantees
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Histogram-Based Approximation of Set-Valued Query-Answers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Combining Histograms and Parametric Curve Fitting for Feedback-Driven Query Result-size Estimation
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Approximate Query Processing Using Wavelets
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
ICICLES: Self-Tuning Samples for Approximate Query Answering
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Incremental Maintenance of Approximate Histograms
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Automating Statistics Management for Query Optimizers
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Automatic tuning of data synopses
Information Systems - Special issue: Best papers from EDBT 2002
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
A sample advisor for approximate query processing
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
The design and architecture of the τ-synopses system
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Synopses reconciliation via calibration in the τ-synopses system
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Optimizing Sample Design for Approximate Query Processing
International Journal of Knowledge-Based Organizations
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Maintaining statistics on multidimensional data distributions is crucial for predicting the run-time and result size of queries and data analysis tasks with acceptable accuracy. To this end a plethora of techniques have been proposed for maintaining a compact data "synopsis" on a single table, ranging from variants of histograms to methods based on wavelets and other transforms. However, the fundamental question of how to reconcile the synopses for large information sources with many tables has been largely unexplored. This paper develops a general framework for reconciling the synopses on many tables, which may come from different information sources. It shows how to compute the optimal combination of synopses for a given workload and a limited amount of available memory. The practicality of the approach and the accuracy of the proposed heuristics are demonstrated by experiments.