Efficient sampling strategies for relational database operations
ICDT Selected papers of the 4th international conference on Database theory
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
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
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
Online query processing: a tutorial
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Accurate estimation of the number of tuples satisfying a condition
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Aqua: A Fast Decision Support Systems Using Approximate Query Answers
VLDB '99 Proceedings of the 25th 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
Query sampling in DB2 Universal Database
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
ACM Transactions on Database Systems (TODS)
Scalable approximate query processing with the DBO engine
ACM Transactions on Database Systems (TODS)
Provenance in Databases: Why, How, and Where
Foundations and Trends in Databases
Turbo-charging estimate convergence in DBO
Proceedings of the VLDB Endowment
The DataPath system: a data-centric analytic processing engine for large data warehouses
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
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As of 2005, sampling has been incorporated in all major database systems. While efficient sampling techniques are realizable, determining the accuracy of an estimate obtained from the sample is still an unresolved problem. In this paper, we present a theoretical framework that allows an elegant treatment of the problem. We base our work on generalized uniform sampling (GUS), a class of sampling methods that subsumes a wide variety of sampling techniques. We introduce a key notion of equivalence that allows GUS sampling operators to commute with selection and join, and derivation of confidence intervals. We illustrate the theory through extensive examples and give indications on how to use it to provide meaningful estimates in database systems.