Abstract

This paper aims to improve the accuracy of query result-size estimations in query optimizers by leveraging the dynamic feedback obtained from observations on the executed query workload. To this end, an approximate "synopsis" of data-value distributions is devised that combines histograms with parametric curve fitting, leading to a specific class of linear splines. The approach reconciles the benefits of histograms, simplicity and versatility, with those of parametric techniques especially the adaptivity to statistically biased and dynamically evolving query workloads. The paper

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