The basis of this book is the fundamental idea of a depth search machine (DSM) model with a foundation built on binary matrices. Processing is handled by the general tasks existence, every, identification, searching, and all. The basic tasks are existence and every, and algorithms are developed for the other tasks depending on their environment. The coverage of topics is wide, including architecture, data structures, algorithms, and applications.

The foundations are developed in chapters 1 and 2. Chapter 1 addresses the initial terminology and architecture, including an extension to parallel or distributed models. Chapter 2 focuses on elementary data structures and processing algorithms for sorting and searching, including complexity considerations.

Characteristic function (CF) databases generalize the data structures from chapter 2. Chapter 3 develops this relational database with a single relation, which is extensible to multirelational databases. An interesting result is that the efficiency of sorting and searching is not affected by any preordering of objects, entities, or relations, thus simplifying memory management. Chapter 4 extends the CF databases to a parallel or distributed environment.

Chapters 5 and 6 are concerned with representing and solving intractable (NP-complete and isomorphic-complete) combinatorial problems in DSMs. Chapters 7 through 10 address application of DSMs to computational geometry, pattern recognition, and image analysis. Chapter 11 demonstrates a flexible and efficient approach to expert system implementation and extensions to huge knowledge bases and to parallel and distributed environments.

The DSM concept presented in this book unifies a wide range of problem domains with a consistent, straightforward, and versatile system. The material is technically rigorous and not for the casual reader. This book is recommended for researchers in alternative architectures, computational geometry and combinatorics, or distributed or parallel knowledge bases and databases.