Providing Better Support for Quanti ed Queries

Relational database systems do not e ectively support complex queries containing quanti ers (quanti ed queries) that are increasingly becoming important in decision support applications. Generalized quanti ers provide an e ective way of expressing such queries naturally. In this paper, we consider the problem of processing quanti ed queries within the generalized quanti er framework. We demonstrate that current relational systems are ill-equipped, both at the language and at the query processing level, to deal with such queries. We also provide insights into the intrinsic di culties associated with processing such queries. We then describe the implementation of a quanti ed query processor, QP, that is based on multidimensional and boolean matrix structures. We provide results of performance experiments run on QP that demonstrate superior performance on quanti ed queries. Our results indicate that it is feasible to augment relational systems with query subsystems like QP for signi cant performance bene ts for quanti ed queries in decision support applications.