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In large data recording and warehousing environments, it is often advantageous to provide fast, approximate answers to queries, whenever possible. Before DBMSs providing highly-accurate approximate answers can become a reality, many new techniques for summarizing data and for estimating answers from summarized data must be developed. This paper introduces two new sampling-based summary statistics, concise samples and counting samples, and presents new techniques for their fast incremental maintenance regardless of the data distribution. We quantify their advantages over standard sample views in terms of the number of additional sample points for the same view size, and hence in providing more accurate query answers. Finally, we consider their application to providing fast approximate answers to hot list queries. Our algorithms maintain their accuracy in the presence of ongoing insertions to the data warehouse.
Gibbons et al. (Mon,) studied this question.