Selectivity Estimation by Batch-Query based Histogram and Parametric Method
Histograms are used extensively for selectivity estimation and approximate query processing. Workload-aware dynamic histograms can self-tune itself based on query feedback without scanning or sampling the underlying datasets in a systematic and comprehensive way. Dynamic histograms allocate more buckets not only for the areas with most skewed data distribution but also according to users' interest. However, it takes long time to 'Warm-up' (i.e., a large number of queries need to be processed before the histogram can provide a satisfactory coverage and accuracy).