introduce a novel technique for estimating queries’ cardinalities using estimated query containment rates. We show that using the proposed technique we improve current cardinality estimation techniques significantly. This is especially the case when there are multiple joins, where the known cardinality estimation techniques suffer from under-estimated results and errors that grow exponentially as the number of joins increases [@joinsHard]. Our technique estimates the cardinalities more robustly (x150/x175 with 4 joins queries, and x1650/x120 with 5 joins queries, compared with PostgreSQL and MSCN, respectively). We compare our technique with PostgreSQL [@postgreSQL], and the pioneering MSCN model [@LearnedCrd], by examining, on the real-world IMDb database [@HowGoodCar], join crossing correlations queries which are known to present a tough challenge to cardinality estimation methods [@HowGoodCar; @crdHard; @JoinCross].

We show that by employing known existing cardinality estimation methods for containment estimation, we can improve on their cardinality estimates as well, without changing the methods themselves. Thus, our novel approach is highly promising for solving the cardinality estimation problem, the “Achilles heel” of query optimization [@crdHard2], a cause of many performance issues [@HowGoodCar].

The rest of this paper is organized as follows. In Section \[Containment Rate Definition\] we define the containment rate problem and in Sections 