Knowledge-based query processing

TL;DR: The principal contribution of the work is the establishment of a conceptual framework for this type of query optimization and the design of an architecture that can grow with the development of additional transformation techniques.

Abstract: Contemporary database query processing systems base their actions principally on "syntactic" considerations, and seek only the most efficacious way of answering a query as originally formulated. An alternative approach seeks to use knowledge of the semantics of the database's application to transform the original query into an alternative form, possibly quite different in its expression, but which is both equivalent to the original (in terms of the set of records from the database that it qualifies) and more efficient to process, given the existing file structures and access methods. The architecture of a system supporting such knowledge-based "semantic" transformations has been developed. It addresses such issues as the kinds of knowledge that should be included in the knowledge base and how it should be expressed, the kinds of transformations that can exploit this knowledge to improve query processing, and the way in which the system as a whole can be organized in the presence of large and intricate knowledge bases and a multiplicity of possible transformation types. This latter structure is based on a multi-processing model, in which each possible transformation is treated as a process, whose priority is assigned by a scheduler embodying a variety of heuristics. The principal contribution of the work is the establishment of a conceptual framework for this type of query optimization and the design of an architecture that can grow with the development of additional transformation techniques.