Multiple comparison procedures
Multiple comparison procedures
Processing aggregate relational queries with hard time constraints
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
VLDB '89 Proceedings of the 15th international conference on Very large data bases
Practical selectivity estimation through adaptive sampling
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Random sampling from hash files
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
New sampling-based summary statistics for improving approximate query answers
SIGMOD '98 Proceedings of the 1998 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
Statistical estimators for relational algebra expressions
Proceedings of the seventh ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Towards estimation error guarantees for distinct values
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Congressional samples for approximate answering of group-by queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A robust, optimization-based approach for approximate answering of aggregate queries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Processing complex aggregate queries over data streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
ICICLES: Self-Tuning Samples for Approximate Query Answering
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
A disk-based join with probabilistic guarantees
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
| Hi-index | 0.00 |
Sampling is now a very important data management tool, to such an extent that an interface for database sampling is included in the latest SQL standard. In this article we reconsider in depth what at first may seem like a very simple problem—computing the error of a sampling-based guess for the answer to a GROUP BY query over a multitable join. The difficulty when sampling for the answer to such a query is that the same sample will be used to guess the result of the query for each group, which induces correlations among the estimates. Thus, from a statistical point-of-view it is very problematic and even dangerous to use traditional methods such as confidence intervals for communicating estimate accuracy to the user. We explore ways to address this problem, and pay particular attention to the computational aspects of computing “safe” confidence intervals.