CoDS: A representative sampling method for relational databases

Database sampling has become a popular approach to handle large amounts of data in a wide range of application areas such as data mining or approximate query evaluation. Using database samples is a potential solution when using the entire database is not cost-e ective, and a balance between the accuracy of the results and the computational cost of the process applied on the large data set is preferred. Existing sampling approaches are either limited to speci c application areas, to single table databases, or to random sampling. In this paper, we propose CoDS: a novel sampling approach targeting relational databases that ensures that the sample database follows the same distribution for specific fields as the original database. In particular it aims to maintain the distribution between tables. We evaluate the performance of our algorithm by measuring the representativeness of the sample with respect to the original database. We compare our approach with two existing solutions, and we show that our method performs faster and produces better results in terms of representativeness.