Knowledge-based systems and solving knowledge integrating problems have seen a great surge of research activity in recent years. Knowledge Integration Methods provides a wide snapshot of building knowledge-based systems, inconsistency measures, methods for handling consistency, and methods for integrating knowledge bases. The book also provides the mathematical background to solving problems of restoring consistency and integrating probabilistic knowledge bases in the integrating process. The research results presented in the book can be applied in decision support systems, semantic web systems, multimedia information retrieval systems, medical imaging systems, cooperative information systems, and more. This text will be useful for computer science graduates and PhD students, in addition to researchers and readers working on knowledge management and ontology interpretation.

The design of knowledge systems is finding myriad applications from corporate databases to general decision support in areas as diverse as engineering, manufacturing and other industrial processes, medicine, business, and economics. In engineering, for example, knowledge bases can be utilized for reliable electric power system operation. In medicine they support complex diagnoses, while in business they inform the process of strategic planning. Programmed securities trading and the defeat of chess champion Kasparov by IBM's Big Blue are two familiar examples of dedicated knowledge bases in combination with an expert system for decision-making.With volumes covering "Implementation," "Optimization," "Computer Techniques," and "Systems and Applications," this comprehensive set constitutes a unique reference source for students, practitioners, and researchers in computer science, engineering, and the broad range of applications areas for knowledge-based systems.

Knowledge Base Systems are an integration of conventional database systems with Artificial Intelligence techniques. They provide inference capabilities to the database system by encapsulating the knowledge of the application domain within the database. Knowledge is the most valuable of all corporate resources that must be captured, stored, re-used and continuously improved, in much the same way as database systems were important in the previous decade. Flexible, extensible, and yet efficient Knowledge Base Systems are needed to capture the increasing demand for knowledge-based applications which will become a significant market in the next decade. Knowledge can be expressed in many static and dynamic forms; the most prominent being domain objects, their relationships, and their rules of evolution and transformation. It is important to express and seamlessly use all types of knowledge in a single Knowledge Base System. Parallel, Object-Oriented, and Active Knowledge Base Systems presents in detail features that a Knowledge Base System should have in order to fulfill the above requirements. Parallel, Object-Oriented, and Active Knowledge Base Systems covers in detail the following topics: Integration of deductive, production, and active rules in sequential database systems. Integration and inter-operation of multiple rule types into the same Knowledge Base System. Parallel rule matching and execution, for deductive, production, and active rules, in parallel Export, Knowledge Base, and Database Systems. In-depth description of a Parallel, Object-Oriented, and Active Knowledge Base System that integrates all rule paradigms into a single database system without hindering performance. Parallel, Object-Oriented, and Active Knowledge Base Systems is intended as a graduate-level text for a course on Knowledge Base Systems and as a reference for researchers and practitioners in the areas of database systems, knowledge base systems and Artificial Intelligence.

This book is the proceedings of a workshop held at Heriot-Watt University in Edinburgh in August 1993. The central theme of the workshop was rules in database systems, and the papers presented covered a range of different aspects of database rule systems. These aspects are reflected in the sessions of the workshop, which are the same as the sections in this proceedings: Active Databases Architectures Incorporating Temporal Rules Rules and Transactions Analysis and Debugging of Active Rules Integrating Graphs/Objects with Deduction Integrating Deductive and Active Rules Integrity Constraints Deductive Databases The incorporation of rules into database systems is an important area of research, as it is a major component in the integration of behavioural information with the structural data with which commercial databases have traditionally been associated. This integration of the behavioural aspects of an application with the data to which it applies in database systems leads to more straightforward application development and more efficient processing of data. Many novel applications seem to need database systems in which structural and behavioural information are fully integrated. Rules are only one means of expressing behavioural information, but it is clear that different types of rule can be used to capture directly different properties of an application which are cumbersome to support using conventional database architectures. In recent years there has been a surge of research activity focusing upon active database systems, and this volume opens with a collection of papers devoted specifically to this topic.

This book is a tribute to Professor Jacek Żurada, who is best known for his contributions to computational intelligence and knowledge-based neurocomputing. It is dedicated to Professor Jacek Żurada, Full Professor at the Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, J.B. Speed School of Engineering, University of Louisville, Kentucky, USA, as a token of appreciation for his scientific and scholarly achievements, and for his longstanding service to many communities, notably the computational intelligence community, in particular neural networks, machine learning, data analyses and data mining, but also the fuzzy logic and evolutionary computation communities, to name but a few. At the same time, the book recognizes and honors Professor Żurada’s dedication and service to many scientific, scholarly and professional societies, especially the IEEE (Institute of Electrical and Electronics Engineers), the world’s largest professional technical professional organization dedicated to advancing science and technology in a broad spectrum of areas and fields. The volume is divided into five major parts, the first of which addresses theoretic, algorithmic and implementation problems related to the intelligent use of data in the sense of how to derive practically useful information and knowledge from data. In turn, Part 2 is devoted to various aspects of neural networks and connectionist systems. Part 3 deals with essential tools and techniques for intelligent technologies in systems modeling and Part 4 focuses on intelligent technologies in decision-making, optimization and control, while Part 5 explores the applications of intelligent technologies.

By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.