Proceedings Of The Decision Tables Symposium

Communications in data processing; How decision tables work; Terminology and structure; Preparing decision tables; Specialized techniques; Decision tables in systems work; Decision tables in programming; Decision tables - implementation and standards.

A comprehensive introduction to mathematical structures essential for Rough Set Theory. The book enables the reader to systematically study all topics of rough set theory. After a detailed introduction in Part 1 along with an extensive bibliography of current research papers. Part 2 presents a self-contained study that brings together all the relevant information from respective areas of mathematics and logics. Part 3 provides an overall picture of theoretical developments in rough set theory, covering logical, algebraic, and topological methods. Topics covered include: algebraic theory of approximation spaces, logical and set-theoretical approaches to indiscernibility and functional dependence, topological spaces of rough sets. The final part gives a unique view on mutual relations between fuzzy and rough set theories (rough fuzzy and fuzzy rough sets). Over 300 excercises allow the reader to master the topics considered. The book can be used as a textbook and as a reference work.

The rapid advancement of semantic web technologies, along with the fact that they are at various levels of maturity, has left many practitioners confused about the current state of these technologies. Focusing on the most mature technologies, Applied Semantic Web Technologies integrates theory with case studies to illustrate the history, current state, and future direction of the semantic web. It maintains an emphasis on real-world applications and examines the technical and practical issues related to the use of semantic technologies in intelligent information management. The book starts with an introduction to the fundamentals—reviewing ontology basics, ontology languages, and research related to ontology alignment, mediation, and mapping. Next, it covers ontology engineering issues and presents a collaborative ontology engineering tool that is an extension of the Semantic MediaWiki. Unveiling a novel approach to data and knowledge engineering, the text: Introduces cutting-edge taxonomy-aware algorithms Examines semantics-based service composition in transport logistics Offers ontology alignment tools that use information visualization techniques Explains how to enrich the representation of entity semantics in an ontology Addresses challenges in tackling the content creation bottleneck Using case studies, the book provides authoritative insights and highlights valuable lessons learned by the authors—information systems veterans with decades of experience. They explain how to create social ontologies and present examples of the application of semantic technologies in building automation, logistics, ontology-driven business process intelligence, decision making, and energy efficiency in smart homes.

In recent years rough set theory has attracted the attention of many researchers and practitioners all over the world, who have contributed essentially to its development and applications. Weareobservingagrowingresearchinterestinthefoundationsofroughsets, including the various logical, mathematical and philosophical aspects of rough sets. Some relationships have already been established between rough sets and other approaches, and also with a wide range of hybrid systems. As a result, rough sets are linked with decision system modeling and analysis of complex systems, fuzzy sets, neural networks, evolutionary computing, data mining and knowledge discovery, pattern recognition, machine learning, and approximate reasoning. In particular, rough sets are used in probabilistic reasoning, granular computing (including information granule calculi based on rough mereology), intelligent control, intelligent agent modeling, identi?cation of autonomous s- tems, and process speci?cation. Methods based on rough set theory alone or in combination with other - proacheshavebeendiscoveredwith awide rangeofapplicationsinsuchareasas: acoustics, bioinformatics, business and ?nance, chemistry, computer engineering (e.g., data compression, digital image processing, digital signal processing, p- allel and distributed computer systems, sensor fusion, fractal engineering), de- sion analysis and systems, economics, electrical engineering (e.g., control, signal analysis, power systems), environmental studies, informatics, medicine, mole- lar biology, musicology, neurology, robotics, social science, software engineering, spatial visualization, Web engineering, and Web mining.

Rough set approach to reasoning under uncertainty is based on inducing knowledge representation from data under constraints expressed by discernibility or, more generally, similarity of objects. Knowledge derived by this approach consists of reducts, decision or association rules, dependencies, templates, or classifiers. This monograph presents the state of the art of this area. The reader will find here a deep theoretical discussion of relevant notions and ideas as well as rich inventory of algorithmic and heuristic tools for knowledge discovery by rough set methods. An extensive bibliography will help the reader to get an acquaintance with this rapidly growing area of research.