This book covers in a great depth the fast growing topic of techniques, tools and applications of soft computing in XML data management. It is shown how XML data management (like model, query, integration) can be covered with a soft computing focus. This book aims to provide a single account of current studies in soft computing approaches to XML data management. The objective of the book is to provide the state of the art information to researchers, practitioners, and graduate students of the Web intelligence, and at the same time serving the information technology professional faced with non-traditional applications that make the application of conventional approaches difficult or impossible.
This book presents an exhaustive and timely review of key research work on fuzzy XML data management, and provides readers with a comprehensive resource on the state-of-the art tools and theories in this fast growing area. Topics covered in the book include: representation of fuzzy XML, query of fuzzy XML, fuzzy database models, extraction of fuzzy XML from fuzzy database models, reengineering of fuzzy XML into fuzzy database models, and reasoning of fuzzy XML. The book is intended as a reference guide for researchers, practitioners and graduate students working and/or studying in the field of Web Intelligence, as well as for data and knowledge engineering professionals seeking new approaches to replace traditional methods, which may be unnecessarily complex or even unproductive.
Research and development surrounding the use of data queries is receiving increased attention from computer scientists and data specialists alike. Through the use of query technology, large volumes of data in databases can be retrieved, and information systems built based on databases can support problem solving and decision making across industries. The Handbook of Research on Innovative Database Query Processing Techniques focuses on the growing topic of database query processing methods, technologies, and applications. Aimed at providing an all-inclusive reference source of technologies and practices in advanced database query systems, this book investigates various techniques, including database and XML queries, spatiotemporal data queries, big data queries, metadata queries, and applications of database query systems. This comprehensive handbook is a necessary resource for students, IT professionals, data analysts, and academicians interested in uncovering the latest methods for using queries as a means to extract information from databases. This all-inclusive handbook includes the latest research on topics pertaining to information retrieval, data extraction, data management, design and development of database queries, and database and XM queries.
In this book, you will find discussions on the newest native XML databases, along with information on working with XML-enabled relational database systems. In addition, XML Data Management thoroughly examines benchmarks and analysis techniques for performance of XML databases. This book is best used by students that are knowledgeable in database technology and are familiar with XML.
This book offers in-depth insights into the rapidly growing topic of technologies and approaches to modeling fuzzy spatiotemporal data with XML. The topics covered include representation of fuzzy spatiotemporal XML data, topological relationship determination for fuzzy spatiotemporal XML data, mapping between the fuzzy spatiotemporal relational database model and fuzzy spatiotemporal XML data model, and consistencies in fuzzy spatiotemporal XML data updating. Offering a comprehensive guide to the latest research on fuzzy spatiotemporal XML data management, the book is intended to provide state-of-the-art information for researchers, practitioners, and graduate students of Web intelligence, as well as data and knowledge engineering professionals confronted with non-traditional applications that make the use of conventional approaches difficult or impossible.
Ongoing advancements in modern technology have led to significant developments in intelligent systems. With the numerous applications available, it becomes imperative to conduct research and make further progress in this field. Intelligent Systems: Concepts, Methodologies, Tools, and Applications contains a compendium of the latest academic material on the latest breakthroughs and recent progress in intelligent systems. Including innovative studies on information retrieval, artificial intelligence, and software engineering, this multi-volume book is an ideal source for researchers, professionals, academics, upper-level students, and practitioners interested in emerging perspectives in the field of intelligent systems.
This book deals with expert evaluation models in the form of semantic spaces with completeness and orthogonality properties (complete orthogonal semantic spaces). Theoretical and practical studies of some researchers have shown that these spaces describe expert evaluations most adequately, and as a result they were often included in more sophisticated models of intellectual systems for decision making and data analysis. Methods for constructing expert evaluation models of characteristics, comparative analysis of these models, studies of structural composition of their sets and constructing of generalized models are described. Models to obtain rating points for objects and groups of objects with qualitative and quantitative characteristics are presented. A number of regression models combining elements of classical and fuzzy regressions are presented. All methods and models developed by the authors and described in the book are illustrated with examples from various fields of human activities. This book meant for scientists in the field of computer science, expert systems, artificial intelligence and decision making; and also for engineers, post-graduate students and students who study the fuzzy set theory and its applications.
This book systemically presents the latest research findings in fuzzy RDF data modeling and management. Fuzziness widely exist in many data and knowledge intensive applications. With the increasing amount of metadata available, efficient and scalable management of massive semantic data with uncertainty is of crucial importance. This book goes to great depth concerning the fast-growing topic of technologies and approaches of modeling and managing fuzzy metadata with Resource Description Framework (RDF) format. Its major topics include representation of fuzzy RDF data, fuzzy RDF graph matching, query of fuzzy RDF data, and persistence of fuzzy RDF data in diverse databases. The objective of the book is to provide the state-of-the-art information to researchers, practitioners, and postgraduates students who work on the area of big data intelligence and at the same time serve as the uncertain data and knowledge engineering professional as a valuable real-world reference.
The authors have consolidated their research work in this volume titled Soft Computing for Data Mining Applications. The monograph gives an insight into the research in the ?elds of Data Mining in combination with Soft Computing methodologies. In these days, the data continues to grow - ponentially. Much of the data is implicitly or explicitly imprecise. Database discovery seeks to discover noteworthy, unrecognized associations between the data items in the existing database. The potential of discovery comes from the realization that alternate contexts may reveal additional valuable information. The rate at which the data is storedis growing at a phenomenal rate. Asaresult,traditionaladhocmixturesofstatisticaltechniquesanddata managementtools are no longer adequate for analyzing this vast collection of data. Severaldomainswherelargevolumesofdataarestoredincentralizedor distributeddatabasesincludesapplicationslikeinelectroniccommerce,bio- formatics, computer security, Web intelligence, intelligent learning database systems,?nance,marketing,healthcare,telecommunications,andother?elds. E?cient tools and algorithms for knowledge discovery in large data sets have been devised during the recent years. These methods exploit the ca- bility of computers to search huge amounts of data in a fast and e?ective manner. However,the data to be analyzed is imprecise and a?icted with - certainty. In the case of heterogeneous data sources such as text and video, the data might moreover be ambiguous and partly con?icting. Besides, p- terns and relationships of interest are usually approximate. Thus, in order to make the information mining process more robust it requires tolerance toward imprecision, uncertainty and exceptions.
