Random sampling with a reservoir
ACM Transactions on Mathematical Software (TOMS)
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
Congressional samples for approximate answering of group-by queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Overcoming Limitations of Sampling for Aggregation Queries
Proceedings of the 17th International Conference on Data Engineering
ICICLES: Self-Tuning Samples for Approximate Query Answering
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Dynamic sample selection for approximate query processing
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
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Approximate query processing is an adequate technique to reduce response times and system load in cases where approximate results suffice. In database literature, sampling has been proposed to evaluate queries approximately by using only a subset of the original data. Unfortunately, most of these methods consider either only certain problems arising due to the use of samples in databases (e.g. data skew) or only join operations involving multiple relations. We describe how well-known sampling techniques dealing with group-by operations can be combined with foreign-key joins such that the join is computed after the generation of the sample. In detail, we show how senate sampling and small group sampling can be combined efficiently with the idea of join synopses. Additionally, we introduce different algorithms which maintain the sample if the underlying data changes. Finally, we prove the superiority of our method to the naive approach in an extensive set of experiments.