output is its cardinality estimation, as described in the leftmost diagram in Figure \[fig:teaser\]. In our proposed approach, we address the problem differently, and we obtain better estimates as described in Section \[Cardinality Evaluation\].

In today’s databases, the answer to a previous query is rarely useful for speeding up new queries, and the work performed in answering past queries is often ignored afterwards. Using the CRN model for predicting containment rates, we are able to change this by revealing the underlying relations between the new queries and the previous ones.

Our new technique for estimating cardinalities mainly relies on two key ideas. The first one is the new framework in which we solve the problem. The second is the use of a *queries pool* that maintains multiple previously executed queries along with their actual cardinalities, as part of the database meta information. The queries pool provides new information that enables our technique to achieve better estimates. Using a containment rate estimation model, we make use of previously executed queries along with their actual cardinalities to estimate the result-cardinality of a new query. This is done with the help of a simple transformation from the problem of containment rate estimation to 