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
