Optimizing Queries Using a Meta-level Database

Abstract

Graph simulation (using graph schemata or data guides) has been successfully proposed as a technique for adding structure to semistructured data. Design patterns for description (such as meta-classes and homomorphisms between schema layers), which are prominent in the object-oriented programming community, constitute a generalization of this graph simulation approach. In this paper, we show description applicable to a wide range of data models that have some notion of object (-identity), and propose to turn it into a data model primitive much like, say, inheritance. We argue that such an extension fills a practical need in contemporary data management. Then, we present algebraic techniques for query optimization (using the notions of described and description queries). Finally, in the semistructured setting, we discuss the pruning of regular path queries (with nested conditions) using description meta-data. In this context, our notion of meta-data extends graph schemata and data guides by meta-level values, allowing to boost query performance and to reduce the redundancy of data.