Practical selectivity estimation through adaptive sampling
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Statistical estimators for aggregate relational algebra queries
ACM Transactions on Database Systems (TODS)
Fixed-precision estimation of join selectivity
PODS '93 Proceedings of the twelfth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Selectivity and cost estimation for joins based on random sampling
Journal of Computer and System Sciences
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Ripple joins for online aggregation
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
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
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Large-Sample and Deterministic Confidence Intervals for Online Aggregation
SSDBM '97 Proceedings of the Ninth International Conference on Scientific and Statistical Database Management
Uncertainty Management for Spatial Data in Databases: Fuzzy Spatial Data Types
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Early accurate results for advanced analytics on MapReduce
Proceedings of the VLDB Endowment
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Statistical estimation and approximate query processing have become increasingly prevalent applications for database systems. However, approximation is usually of little use without some sort of guarantee on estimation accuracy, or "confidence bound." Analytically deriving probabilistic guarantees for database queries over sampled data is a daunting task, not suitable for the faint of heart, and certainly beyond the expertise of the typical database system end-user. This paper considers the problem of incorporating into a database system a powerful "plug-in" method for computing confidence bounds on the answer to relational database queries over sampled or incomplete data. This statistical tool, called the bootstrap, is simple enough that it can be used by a data-base programmer with a rudimentary mathematical background, but general enough that it can be applied to almost any statistical inference problem. Given the power and ease-of-use of the bootstrap, we argue that the algorithms presented for supporting the bootstrap should be incorporated into any database system which is intended to support analytic processing.