Towards Realistic Sampling: Generating Dependencies in a Relational Database

Managing large amounts of information is one of the most expensive, time-consuming and non-trivial activities and it usually requires expert knowledge. In a wide range of ap-plication areas, such as data mining, histogram construc-tion, approximate query evaluation, and software validation, handling exponentially growing databases has become a dif-ficult challenge, and a subset of the data is generally pre-ferred. As a solution to the current challenges in managing large amounts of data, database sampling from the opera-tional data available has proved to be a powerful technique. However, none of the existing sampling approaches consider the dependencies between the data in a relational database. In this paper, we propose a novel approach towards con-structing a realistic testing environment, by analyzing the distribution of data in the original database along these de-pendencies before sampling, so that the sample database is representative to the original database