This book illustrates important ways in which new parallel hardware is being used to improve the speed and usefulness of a variety of information systems. It contains thirteen original papers that survey the latest trends in performance enhancing architectures for smart information systems.

The machines featured in the text have been designed to support information systems ranging from relational databases to semantic networks and other artificial intelligence paradigms. In addition, many of the projects illustrated in the book contain generic architectural ideas that support higher-level requirements by using semantics-free hardware designs.

The case studies describe add-on machines and performance-enhancing units that employ parallel hardware to speed up database operations. Other case studies show how high-performance computers support database and related software, even though some platforms were originally designed for scientific or numeric applications. The last three chapters give examples of machines that are deliberately designed to speed up a particular knowledge representation formalism or a particular AI problem solving paradigm. The information presented throughout this book will help all those engaged in the design or use of high-performance architectures for nonnumeric (i.e., symbolic) applications.

Mahdi Abdelguerfi is the author of Emerging Trends in Database and Knowledge Based Machines: The Application of Parallel Architectures to Smart Information Systems, published by Wiley. Simon Lavington is the author of Emerging Trends in Database and Knowledge Based Machines: The Application of Parallel Architectures to Smart Information Systems, published by Wiley.