Optimizing Queries in a Logic-based Information Integration System

TL;DR

This paper presents an optimization algorithm for transforming high-level queries into efficient low-level Prolog code within the SINTAGMA information integration system, enhancing semantic and structural query efficiency.

Contribution

It introduces a novel query optimization method that improves execution plans in a logic-based information integration framework.

Findings

The optimization algorithm improves query execution efficiency.

Structural and statistical information enhances plan optimization.

Implementation details demonstrate practical effectiveness.

Abstract

The SINTAGMA information integration system is an infrastructure for accessing several different information sources together. Besides providing a uniform interface to the information sources (databases, web services, web sites, RDF resources, XML files), semantic integration is also needed. Semantic integration is carried out by providing a high-level model and the mappings to the models of the sources. When executing a query of the high level model, a query is transformed to a low-level query plan, which is a piece of Prolog code that answers the high-level query. This transformation is done in two phases. First, the Query Planner produces a plan as a logic formula expressing the low-level query. Next, the Query Optimizer transforms this formula to executable Prolog code and optimizes it according to structural and statistical information about the information sources. This article discusses the main ideas of the optimization algorithm and its implementation.