Although these methods have been proven to be among the most accurate estimators known in statistic, they have not been considered for selectivity estimation of database queries, so far. We first show how to generate kernel estimators that deliver accurate approximate selectivities of queries. Thereafter, we reveal that two parameters, the number of samples and the so-called smoothing parameter, are important for the accuracy of both kernel estimators and histogram estimators. We first present the optimal smoothing parameter as a function of the number of samples and show how to compute approximations of the optimal parameter. Moreover, we propose a new selectivity estimator that can be viewed as an hybrid of histogram and kernel estimators.
Experimental results show the performance of different estimators in practice.
We found in our experiments that kernel estimators are most efficient for continuously distributed data sets, whereas for our real data sets the hybrid technique is most promising.