Rule-based optimizers and optimizer generators use rules to specify query transformations. Rules act directly on query representations, which typically are based on query algebras. But most algebras complicate rule formulation, and rules over these algebras must often resort to calling to externally defined bodies of code. Code makes rules difficult to formulate, prove correct and reason about, and therefore compromises the effectiveness of rule-based systems. In this paper we present KOLA; a combinator-based algebra designed to simplify rule formulation. KOLA is not a user language, and KOLA's variable-free queries are difficult for humans to read. But KOLA is an effective internal algebra because its combinatorstyle makes queries manipulable and structurally revealing. As a result, rules over KOLA queries are easily expressed without the need for supplemental code. We illustrate this point, first by showing some transformations that despite their simplicity, require head and body rou...

The diversity and distributed nature of the resources available in the semantic web poses significant challenges when these are used to help automatically build an ontology. One persistent and pervasive problem is that of the resolution or elimination of coreference that arises when more than one identifier is used to refer to the same resource. Tackling this problem is crucial...

The paper describes a rule-based query optimizer for object-oriented databases. The originality of the approach is through a multi-level logic programming used to model the variety of knowledge contained in the query optimizer in an explicit, declarative and transparent way. Our approach offers means of abstraction for expressing various kinds of knowledge involved in a query optimizer. It also offers techniques for structuring them according to both generality levels and knowledge content, i.e. meta-levels. We present a programming technique that allows to write modules which can be at various meta-levels. To illustrate these ideas, we show how multi-level programming can be used to model a query optimizer for an object-oriented database. Among the various kinds of knowledge involved, we have (besides the queries themselves - first or object level) techniques for query manipulations and transformation, as well as cost models (second or meta- level), techniques for combining transforma...

Query optimizers generate plans to retrieve data specified by queries. Query optimization for object databases (i.e., object-oriented and object-relational databases) is an immature field, and stands to benefit from adaptation of techniques that have proved useful for relations. One technique uses query-to-query transformations to rewrite queries into queries that are potentially more amenable to plan generation. For transformations to be useful, they must preserve the semantics of the queries they rewrite (correctness) and usually result in queries that generate better plans (effectiveness). Object databases complicate the expression of correct and effective transformations. Transformation correctness is problematic even for relational queries. Especially error-prone are transformations that rewrite complex nested queries (queries containing other queries) or queries that return duplicates. Objects make correctness more difficult because object queries can be far more compl...

The paper describes a rule-based query optimizer for object-oriented databases. The originality of the approach is through a multi-level logic programming used to model the variety of knowledge contained in the query optimizer in an explicit, declarative and transparent way. Our approach offers means of abstraction for expressing various kinds of knowledge involved in a query optimizer. It also offers techniques for structuring them according to both generality levels and knowledge content, i.e. meta-levels. We present a programming technique that allows to write modules which can be at various meta-levels. To illustrate these ideas, we show how multi-level programming can be used to model a query optimizer for an object-oriented database.