A database view is a portion of the data structured in a way suitable to a specific application. Updates on views must be translated into updates on the underlying database. This paper studies the translation process in the relational model.The procedure is as follows: first, a "complete" set of updates is defined such that (i) together with every update the set contains a "return" update, that is, one that brings the view back to the original state; (ii) given two updates in the set, their composition is also in the set.To translate a complete set, we define a mapping called a "translator,"that associates with each view update a unique database update called a "translation."The constraint on a translation is to take the database to a state mapping onto the updated view. The constraint on the translator is to be a morphism.We propose a method for defining translators. Together with the user-defined view, we define a "complementary" view such that the database could be computed from the view and its complement. We show that a view can have many different complements and that the choice of a complement determines an update policy. Thus, we fix a view complement and we define the translation of a given view update in such a way that the complement remains invariant ("translation under constant complemen$'). The main result of the paper states that, given a complete set U of view updates, U has a translator if and only if U is translatable under constant complement.

Several methods for implementing database queries expressed as logical rules are given and they are compared for efficiency. One method, called "magic sets," is a general algorithm for rewriting logical rules so that they may be implemented bottom-UP (= forward chaining) in a way that cuts down on the irrelevant facts that are generated.The advantage of this scheme is that by working bottom-up, we can take advantage of efficient methods for doing massive joins. Two other methods are ad hoc ways of implementing "linear" rules, i.e., rules where at most one predicate in any body is recursive. These methods are

No abstract

This paper surveys and compares various strategies for processing logic queries in relational databases. The survey and comparison is limited to the case of Horn Clauses with evaluable predicates but without function symbols. The paper is organized in three parts. In the first part, we introduce the main concepts and definitions. In the second, we describe the various strategies. For each strategy, we give its main characteristics, its application range and a detailed description. We also give an example of a query evaluation. The third part of the paper compares the strategies on performance grounds. We first present a set of sample rules and queries which are used for the performance comparisons, and then we characterize the data. Finally, we give an analytical solution for each query/rule system. Cost curves are plotted for specific configurations of the data.

This paper surveys and compares various strategies for processmg logic queries m relational databases The survey and comparison 1s hmlted to the case of Horn Clauses with evaluable predicates but wrthout function symbols The paper 1s organized m three parts In the first part, we introduce the mam concepts and defimtrons In the second, we describe the vanous strategies For each strategy, we give Its mam charactenstms, Its apphcatron range and a detailed descnptron We also give an example of a query evaluatron The third part of the paper compares the strategies on performance grounds We first present a set of sample rules and queries which are used for the performance compansons, and then we characterize the data Finally, we give an analytrcal solution for each query/rule system Cost curves are plotted for specific configurations of the data