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
Selectivity and cost estimation for joins based on random sampling
Journal of Computer and System Sciences
The space complexity of approximating the frequency moments
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
A sequence of series for the Lambert W function
ISSAC '97 Proceedings of the 1997 international symposium on Symbolic and algebraic computation
Tracking join and self-join sizes in limited storage
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Selectivity estimation using probabilistic models
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
LEO - DB2's LEarning Optimizer
Proceedings of the 27th 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
Convex Optimization
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Consistently estimating the selectivity of conjuncts of predicates
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Machine Learning
ISOMER: Consistent Histogram Construction Using Query Feedback
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Extended wavelets for multiple measures
ACM Transactions on Database Systems (TODS)
The dichotomy of conjunctive queries on probabilistic structures
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
The history of histograms (abridged)
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Sketches for size of join estimation
ACM Transactions on Database Systems (TODS)
Conditioning probabilistic databases
Proceedings of the VLDB Endowment
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
Diagnosing Estimation Errors in Page Counts Using Execution Feedback
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
General Database Statistics Using Entropy Maximization
DBPL '09 Proceedings of the 12th International Symposium on Database Programming Languages
Consistent histograms in the presence of distinct value counts
Proceedings of the VLDB Endowment
Understanding cardinality estimation using entropy maximization
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
World-set decompositions: expressiveness and efficient algorithms
ICDT'07 Proceedings of the 11th international conference on Database Theory
Asymptotic conditional probabilities for conjunctive queries
ICDT'05 Proceedings of the 10th international conference on Database Theory
Entropy-based histograms for selectivity estimation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Cardinality estimation is the problem of estimating the number of tuples returned by a query; it is a fundamentally important task in data management, used in query optimization, progress estimation, and resource provisioning. We study cardinality estimation in a principled framework: given a set of statistical assertions about the number of tuples returned by a fixed set of queries, predict the number of tuples returned by a new query. We model this problem using the probability space, over possible worlds, that satisfies all provided statistical assertions and maximizes entropy. We call this the Entropy Maximization model for statistics (MaxEnt). In this article we develop the mathematical techniques needed to use the MaxEnt model for predicting the cardinality of conjunctive queries.