Fernando Sáenz-Pérez

Fernando Sáenz-Pérez

This paper proposes the use of Constraint Logic Programming (CLP) to model SQL queries in a data-independent abstract layer by focusing on some semantic properties for signalling possible errors in such queries. First, we define a translation from SQL to Datalog, and from Datalog to CLP, so that solving this CLP program will give information about inconsistency, tautology, and possible simplifications. We use different constraint domains which are mapped to SQL types, and propose them to cooperate for improving accuracy. Our approach leverages a deductive system that includes SQL and Datalog, and we present an implementation in this system which is currently being tested in classroom, showing its advantages and differences with respect to other approaches, as well as some performance data. This paper is under consideration for acceptance in TPLP.

Fernando Sáenz-Pérez

Hypothetical Datalog is based on an intuitionistic semantics rather than on a classical logic semantics, and embedded implications are allowed in rule bodies. While the usual implication (i.e., the neck of a Horn clause) stands for inferring facts, an embedded implication plays the role of assuming its premise for deriving its consequence. A former work introduced both a formal framework and a goal-oriented tabled implementation, allowing negation in rule bodies. While in that work positive assumptions for both facts and rules can occur in the premise, negative assumptions are not allowed. In this work, we cover this subject by introducing a new concept: a restricted predicate, which allows negative assumptions by pruning the usual semantics of a predicate. This new setting has been implemented in the deductive system DES.

Fernando Sáenz-Pérez

This work presents how persistent predicates have been included in the in-memory deductive system DES by relying on external SQL database management systems. We introduce how persistence is supported from a user-point of view and the possible applications the system opens up, as the deductive expressive power is projected to relational databases. Also, we describe how it is possible to intermix computations of the deductive engine and the external database, explaining its implementation and some optimizations. Finally, a performance analysis is undertaken, comparing the system with current relational database systems.