Random sampling for histogram construction: how much is enough?
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
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
The Aqua approximate query answering system
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Towards estimation error guarantees for distinct values
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Learning table access cardinalities with LEO
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Maintenance of Materialized Views of Sampling Queries
Proceedings of the Eighth International Conference on Data Engineering
Sampling-Based Estimation of the Number of Distinct Values of an Attribute
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Large-Sample and Deterministic Confidence Intervals for Online Aggregation
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
A bi-level Bernoulli scheme for database sampling
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
CORDS: automatic discovery of correlations and soft functional dependencies
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Robust query processing through progressive optimization
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A dip in the reservoir: maintaining sample synopses of evolving datasets
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Generating targeted queries for database testing
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
StatAdvisor: recommending statistical views
Proceedings of the VLDB Endowment
Consistent histograms in the presence of distinct value counts
Proceedings of the VLDB Endowment
Bernoulli sampling based (ε, δ)-approximate aggregation in large-scale sensor networks
INFOCOM'10 Proceedings of the 29th conference on Information communications
The VC-dimension of SQL queries and selectivity estimation through sampling
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Re-optimizing data-parallel computing
NSDI'12 Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation
Optimizing queries with expensive video predicates in cloud environment
Concurrency and Computation: Practice & Experience
Balancing reducer skew in MapReduce workloads using progressive sampling
Proceedings of the Third ACM Symposium on Cloud Computing
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Accurate cardinality estimation is critically important to high-quality query optimization. It is well known that conventional cardinality estimation based on histograms or similar statistics may produce extremely poor estimates in a variety of situations, for example, queries with complex predicates, correlation among columns, or predicates containing user-defined functions. In this paper, we propose a new, general cardinality estimation technique that combines random sampling and materialized view technology to produce accurate estimates even in these situations. As a major innovation, we exploit feedback information from query execution and process control techniques to assure that estimates remain statistically valid when the underlying data changes. Experimental results based on a prototype implementation in Microsoft SQL Server demonstrate the practicality of the approach and illustrate the dramatic effects improved cardinality estimates may have.