Adaptive selectivity estimation using query feedback
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Balancing histogram optimality and practicality for query result size estimation
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 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
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
Self-tuning histograms: building histograms without looking at data
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
Optimal histograms for hierarchical range queries (extended abstract)
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
STHoles: a multidimensional workload-aware histogram
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Fast algorithms for hierarchical range histogram construction
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Exploiting statistics on query expressions for optimization
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Dynamic multidimensional histograms
Proceedings of the 2002 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
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
LEO - DB2's LEarning Optimizer
Proceedings of the 27th International Conference on Very Large Data Bases
Histogramming Data Streams with Fast Per-Item Processing
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Consistently estimating the selectivity of conjuncts of predicates
VLDB '05 Proceedings of the 31st international conference on Very large data bases
ISOMER: Consistent Histogram Construction Using Query Feedback
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Approximation and streaming algorithms for histogram construction problems
ACM Transactions on Database Systems (TODS)
Extended wavelets for multiple measures
ACM Transactions on Database Systems (TODS)
Cardinality estimation using sample views with quality assurance
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
The history of histograms (abridged)
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
SASH: a self-adaptive histogram set for dynamically changing workloads
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Diagnosing Estimation Errors in Page Counts Using Execution Feedback
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Histograms and Wavelets on Probabilistic Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Workload-optimal histograms on streams
ESA'05 Proceedings of the 13th annual European conference on Algorithms
Understanding cardinality estimation using entropy maximization
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Understanding cardinality estimation using entropy maximization
ACM Transactions on Database Systems (TODS)
Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches
Foundations and Trends in Databases
Histograms as statistical estimators for aggregate queries
Information Systems
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Self-tuning histograms have been proposed in the past as an attempt to leverage feedback from query execution. However, the focus thus far has been on histograms that only store cardinalities. In this paper, we study consistent histogram construction from query feedback that also takes distinct value counts into account. We first show how the entropy maximization (EM) principle can be leveraged to identify a distribution that approximates the data given the execution feedback making the least additional assumptions. This EM model that takes both distinct value counts and cardinalities into account. However, we find that it is computationally prohibitively expensive. We thus consider an alternative formulation for consistency -- for a given query workload, the goal is to minimize the L2 distance between the true and estimated cardinalities. This approach also handles both cardinalities and distinct values counts. We propose an efficient one-pass algorithm with several theoretical properties modeling this formulation. Our experiments show that this approach produces similar improvements in accuracy as the EM based approach while being computationally significantly more efficient.