A hybrid estimator for selectivity estimation

Traditional sampling-based estimators infer the actual selectivity of a query based purely on runtime information gathering, excluding the previously collected information, which underutilizes the information available. Table-based and parametric estimators extrapolate the actual selectivity of a query based only on the previously collected information, ignoring on-line information, which results in inaccurate estimation in a frequently updated environment. We propose a novel hybrid estimator that utilizes and optimally combines the on-line and previously collected information. Theoretical analysis demonstrates that the on-line and previously collected information is complementary and that the comprehensive utilization of the on-line and previously collected information is of value for further performance improvement. Our theoretical results are validated by a comprehensive experimental study using a practical database, in the presence of insert, delete, and update operations. The hybrid approach is very promising in the sense that it provides the adaptive mechanism that allows the optimal combination of information obtained from different sources in order to achieve a higher estimation accuracy and reliability.