A Simpler Approach to Set Comparison Queries in SQL

The current specification of the SQL standard fails to support users adequately in formulating complex queries involving set comparison that tend to arise in on-line analytical processing (OLAP) situations. Such queries must be formulated using correlated subqueries and the NOT EXISTS function which present an overwhelming challenge to both casual as well as everyday SQL users. This paper presents a simpler approach for teaching users how to formulate in SQL complex set comparison queries encountered in ad-hoc decision making scenarios

Converting Paradoxs QBE Set Queries Into Access 2000 SQL

One of the most important promises of the move to an SQL-based accounting software package has been that it frees the accountant from the necessity of resorting to a programmer when retrieving information from the organization's database in response to unanticipated managerial needs. That promise is founded, in part, on the availability of a very high-level, visual relational query language interface known as Query By Example (QBE). Unfortunately, the implementation of QBE in Microsoft Access 2000 fails to support users in formulating complex queries involving set comparison that tend to arise in on-line analytical processing (OLAP) situations. And, while Paradoxs implementation of QBE makes the formulation of such queries quite intuitive, its built-in SQL translation feature fails to provide a clue on how to convert such queries into SQL. This paper presents a systematic approach based on formulating complex set queries in Paradoxs richer QBE notation and translating them into SQL queries that can be handled by Access 2000

Providing Better Support for a Class of Decision Support Queries

Relational database systems do not effectively support complex queries containing quantifiers (quantified queries) that are increasingly becoming important in decision support applications. Generalized quantifiers provide an effective way of expressing such queries naturally. In this paper, we consider the problem of processing quantified queries within the generalized quantifier 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 difficulties associated with processing such queries. We then describe the implementation of a quantified query processor, Q 2 P, that is based on multidimensional and boolean matrix structures. We provide results of performance experiments run on Q 2 P that demonstrate superior performance on quantified queries. Our results indicate that it is feasible to augment relational systems with query subsystems l..

Abstract Providing Better Support for a Class of Decision Support Queries

Relational database systems do not effectively support complex queries containing quantifiers (quantified queries) that are increasingly becoming important in decision support applications. Generalized quantifiers provide an effective way of expressing such queries naturally. In this paper, we consider the problem of processing quantified queries within the generalized quantifier 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 difficulties associated with processing such queries. We then describe the implementation of a quantified query processor, Q2P, that is based on multidimensional and boolean matrix structures. We provide results of performance experiments run on Q2P that demonstrate e superior performance on quantified queries. Our results indicate that it is feasible to augment relational systems with query subsystems like Q2P for significant performance benefits for quantified queries in decision support applications.