Grey Information: Theory and Practical Applications (Advanced Information and Knowledge Processing)

In case you are wondering whether to buy this book, I have a very clear recommendation for you: DO NOT BUY THIS BOOK. The reasons for this recommendation are as follows. Consider Chapter 9.6, which is on "Stock-Market-Like Predictions". This chapter is a complete disaster. The definition of a "zigzaged line" is completely misleading. One has to go back to Chapter 5.4 to get a (halfway) correct definition. The authors then go on to Example 9.6.1 where they do some numerical calculations. However, the raw data on which their calculations are based on is only given in the form of Figure 9.4, i.e. in \*graphical\* form. This is, of course, completely useless for someone who wants to repeat the calculations himself: as an input, one needs numbers not pictures to do that! Finally, the authors show in Figure 9.5 the predicted values for their numerical example. Well, this is nice but pretty useless since nobody is interested in predicting \*something\*, but in predicting \*accurately\*. In this sense, it would be more insightful to see how accurate the predictions were. The authors do not provide this information. The part of the chapter is titled "Stock-Market-Like Predictions", so I thought it might be nice address the question of accuracy in this context if the authors themselves don't do it. I wrote a little program and tested the predictions with data on the Dow Jones Industrial Average from 1960 to 2007. The result was disappointing. Tossing a coin would have been more precise. Sometimes the predictions were very good, sometimes very bad. Furthermore, the writing style is really bad. The authors have serious problems describing things clearly in the English language. Maybe this is because English is not their mother-tongue, but this is a reason, not an excuse. This statement holds especially when one considers the book's high price. With this price, one should expect to get higher quality at least in this respect. The book's back cover says that it will be of interest to students and researchers in "information and systems sciences and management sciences, and to those working in applied areas such as geo-science, engineering, agriculture, medicine, biosciences and others". In short, the authors say that this book is pretty much good for everyone -- great! Unfortunately, I cannot agree with the author's enthusiasm. I would not recommend to use the book's methods in any of the above fields, least of all in medicine where lifes are at stake!

There are two main approaches to knowledge management (KM), the process-centred approach which treats KM as an interpersonal communication process and the product-centred approach which focuses on the artefacts for knowledge, i.e. the documents, their creation and reuse in corporate computer-based systems. Knowledge Asset Management presents a knowledge asset-centric approach which fuses the previous two approaches together. It provides a conceptual framework to guide managers in the planning and development of the initiative and presents a methodology for organisations to: define and document their knowledge management strategy.- audit and design business processes that enhance and facilitate corporate learning.- facilitate knowledge sharing between people in the organisation.- measure and evaluate the quality and value of the organisation's intellectual capital. The book also introduces a way for developing an intranet-based environment to support: the collection and classification of internal and external information.- reuse of stored knowledge using flexible and customisable knowledge navigators and advanced search mechanisms including keyword and concept-based searching (e.g. visualization of the information space).- collaboration via on-line workspaces. Knowledge Asset Management gives an in-depth look at the technologies and methodologies required for knowledge management. Written by four highly experienced consultants in the field, the books also includes case studies showing how the principles work in practice. "One of the rare books today on Knowledge Management that addresses the leveraging of an organization's intellectual assets by using an integrative and holistic approach. Well worth reading!" Michael Stankosky, Professor of Knowledge Management and Co-founder/co-director of the Institute for Knowledge Management, The George Washington University "This book is a useful illustration of Knowledge Management implementation principles: it synthesizes theoretical and pragmatic approaches to the subject and does a competent job of embracing the various dimensions of a Knowledge Management initiative." Daniele Chauvel, Director, European Center for Knowledge Management; Business School Marseille-Provence "For those organisations who wish to take a strategic view of knowledge management, this book shows how they can take KM to the next level - not driven by a technology solution but based on the strategy and needs of the business." Marc Auckland, Chief Learning Officer and Head of the BT Academy, BT "The KM method proposed in this book enables enterprises to exploit their knowledge more effectively by making it easily available to employees and by facilitating the exchange and integration of information used by knowledge workers in a variety of business situations" Ciro Maddaloni, SOGEI S.p.A., Gruppo Telecom Italia.

Rapid formation and development of new theories of systems science have become an important part of modern science and technology. For - ample, since the 1940s, there have appeared systems theory, information theory, fuzzy mathematics, cybernetics, dissipative structures, synergetics, catastrophe theory, chaos theory, bifurcations, ultra circulations, dynamics, and many other systems theories. Grey systems theory is also one of such systems theories that appeared initially in the 1980s. When the research of systems science and the method and technology of systems engineering are applied in various traditional disciplines, such as management science, decision science, and various scienti?c disciplines, a whole new group of new results and breakthroughs are obtained. Such a historical background has provided the environment and soil for grey systems theory to form and to develop rapidly in the past 20-plus years. More speci?cally, in 1982, Professor Deng Ju-Long published the?rst research paper in the area of grey systems in the international journal entitled Systems and Control Letters, published by North-Holland Co. His paper was titled “Control Problems of Grey Systems. ” The publication of this paper signalled the birth of grey systems theory after many years of e ective research of the founding father. This new theory soon caught the attention of the international academic community and practitioners of science. Many well-known scholars, such as Chinese academicians Qian Xueshen, Song Jian, and Zhang Zhongjun. Professor Roger W.

Uncertainty Handling and Quality Assessment in Data Mining provides an introduction to the application of these concepts in Knowledge Discovery and Data Mining. It reviews the state-of-the-art in uncertainty handling and discusses a framework for unveiling and handling uncertainty. Coverage of quality assessment begins with an introduction to cluster analysis and a comparison of the methods and approaches that may be used. The techniques and algorithms involved in other essential data mining tasks, such as classification and extraction of association rules, are also discussed together with a review of the quality criteria and techniques for evaluating the data mining results. This book presents a general framework for assessing quality and handling uncertainty which is based on tested concepts and theories. This framework forms the basis of an implementation tool, 'Uminer' which is introduced to the reader for the first time. This tool supports the key data mining tasks while enhancing the traditional processes for handling uncertainty and assessing quality. Aimed at IT professionals involved with data mining and knowledge discovery, the work is supported with case studies from epidemiology and telecommunications that illustrate how the tool works in 'real world' data mining projects. The book would also be of interest to final year undergraduates or post-graduate students looking at: databases, algorithms, artificial intelligence and information systems particularly with regard to uncertainty and quality assessment.

Rapid formation and development of new theories of systems science have become an important part of modern science and technology. For - ample, since the 1940s, there have appeared systems theory, information theory, fuzzy mathematics, cybernetics, dissipative structures, synergetics, catastrophe theory, chaos theory, bifurcations, ultra circulations, dynamics, and many other systems theories. Grey systems theory is also one of such systems theories that appeared initially in the 1980s. When the research of systems science and the method and technology of systems engineering are applied in various traditional disciplines, such as management science, decision science, and various scienti?c disciplines, a whole new group of new results and breakthroughs are obtained. Such a historical background has provided the environment and soil for grey systems theory to form and to develop rapidly in the past 20-plus years. More speci?cally, in 1982, Professor Deng Ju-Long published the ?rst research paper in the area of grey systems in the international journal entitled Systems and Control Letters, published by North-Holland Co. His paper was titled Control Problems of Grey Systems. The publication of this paper signalled the birth of grey systems theory after many years of e ective research of the founding father. This new theory soon caught the attention of the international academic community and practitioners of science. Many well-known scholars, such as Chinese academicians Qian Xueshen, Song Jian, and Zhang Zhongjun. Professor Roger W. Many organizations have an urgent need of mining their multiple databases inherently distributed in branches (distributed data). In particular, as the Web is rapidly becoming an information flood, individuals and organizations can take into account low-cost information and knowledge on the Internet when making decisions. How to efficiently identify quality knowledge from different data sources has become a significant challenge. This challenge has attracted a great many researchers including the au­ thors who have developed a local pattern analysis, a new strategy for dis­ covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis deliv­ ers high-performance pattern discovery from multiple databases. There has been considerable progress made on multi-database mining in such areas as hierarchical meta-learning, collective mining, database classification, and pe­ culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these inter­ esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, dis­ tributed data analysis, machine learning, and anyone else who is interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might also involve knowledge discovery in databases and data mining. A new economy is emerging. An economy that is transforming the fundamental rules of business. An economy based on exploiting knowledge and innovation. An economy where knowledge is the main source of wealth for regions, nations, enter­ prises and people. This new economy is based on economic values far removed from those of the industrial economy. Value has shifted towards intangibles and in particular towards increasing value by incorporating knowledge into services and products. The advent of this new economy is rapidly changing the role and structure of global business. Winning enterprises are those best able to harness the benefits and opportunities of information and communication technology, capitalize on their knowledge base and move at the speed of the market. Knowledge management lies at the heart of the European Community's competi­ tiveness strategy. The European Commission facilitates and supports applied research in knowledge management through its Information Society Technologies (1ST) programme, a major theme of research and technological development within the European Union's Research and Technology Development Framework Programme. Specifically, the New Methods of Work and Electronic Commerce action of the 1ST programme supports long-term applied research in areas combin­ ing technological innovation with new work practices and advanced business and work models. Ontological Engineering refers to the set of activities that concern the ontology development process, the ontology life cycle, the methods and methodologies for building ontologies, and the tool suites and languages that support them. During the last decade, increasing attention has been focused on ontologies and Ontological Engineering. Ontologies are now widely used in Knowledge Engineering, Artificial Intelligence and Computer Science; in applications related to knowledge management, natural language processing, e-commerce, intelligent integration information, information retrieval, integration of databases, b- informatics, and education; and in new emerging fields like the Semantic Web. Primary goals of this book are to acquaint students, researchers and developers of information systems with the basic concepts and major issues of Ontological Engineering, as well as to make ontologies more understandable to those computer science engineers that integrate ontologies into their information systems. We have paid special attention to the influence that ontologies have on the Semantic Web. Pointers to the Semantic Web appear in all the chapters, but specially in the chapter on ontology languages and tools.

The Web has emerged as a large, distributed data repository, and information on the Internet and in existing transaction databases can be analyzed for commercial gains in decision making. Therefore, how to efficiently identify quality knowledge from different data sources uncovers a significant challenge. This challenge has attracted wide interest from both academia and the industry. Knowledge Discovery in Multiple Databases provides a comprehensive introduction to the latest advancements in multi-database mining, and presents a local-pattern analysis framework for pattern discovery from multiple data sources. Based on this framework, data preparation techniques in multiple databases, an application-independent database classification for data reduction, and efficient algorithms for pattern discovery from multiple databases are described. Knowledge Discovery in Multiple Databases is suitable for researchers, professionals and students in data mining, distributed data analysis, and machine learning, who are interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might involve knowledge discovery in databases and data mining.

Ontologies provide a common vocabulary of an area and define, with different levels of formality, the meaning of the terms and the relationships between them. Ontological engineering refers to the set of activities concerning the ontology development process, the ontology life cycle, the methods and methodologies for building ontologies, and the tool suites and languages that support them. During the last decade, increasing attention has been focused on ontologies. Ontologies are now widely used in knowledge engineering, artificial intelligence and computer science; in applications related to areas such as knowledge management, natural language processing, e-commerce, intelligent information integration, bio-informatics, education; and in new emerging fields like the semantic web. The book presents the major issues of ontological engineering and describes the most outstanding ontologies currently available. It covers the practical aspects of selecting and applying methodologies, languages, and tools for building ontologies. Ontological Engineering will be of great value to students and researchers, and to developers who want to integrate ontologies in their information systems. The recent explosive growth of our ability to generate and store data has created a need for new, scalable and efficient, tools for data analysis. The main focus of the discipline of knowledge discovery in databases is to address this need. Knowledge discovery in databases is the fusion of many areas that are concerned with different aspects of data handling and data analysis, including databases, machine learning, statistics, and algorithms. Each of these areas addresses a different part of the problem, and places different emphasis on different requirements. For example, database techniques are designed to efficiently handle relatively simple queries on large amounts of data stored in external (disk) storage. Machine learning techniques typically consider smaller data sets, and the emphasis is on the accuracy ofa relatively complicated analysis task such as classification. The analysis of large data sets requires the design of new tools that not only combine and generalize techniques from different areas, but also require the design and development ofaltogether new scalable techniques. Grey Information: Theory and Practical Applications is a crystallization of the authors' work over the last twenty-five years. The book covers the latest advances in grey information and systems research, providing a state-of-the-art overview of this important field. Covering the theoretical foundation, fundamental methods and main topics in grey information and systems research, this book includes all the elementary concepts: basic principles, grey numbers and their operations, grey equations and matrices, operators of sequences and generations of grey sequences, grey incidence analysis, grey clusters and grey statistical evaluations, grey systems modeling, grey combined models, grey prediction, grey decisions, grey programming, grey input and output and grey controls, etc. The book will be of interest to advanced students and researchers in a wide range of fields including information and systems sciences and management sciences, and to those working in applied areas such as geo-science, engineering, agriculture, medicine, biosciences and others. The Internet And Wireless Communication Networks Are Transforming The Way Society Handles The Explosive Growth And The Dwindling Half-life Of Environmentally Relevant Information. How Can We Leverage New Technologies To Advocate Sustainability And The Protection Of Natural Ecosystems? This Book Presents An Interdisciplinary Investigation Of This Question, Combining Theoretical Foundations Of Environmental Online Communication With Pioneering Conceptual Work And Case Studies Of Successful Information Systems.--book Jacket. Part I. Raising Environmental Awareness -- Part Ii. Environmental Science -- Part Iii. Corporate Responsibility -- Part Iv. Networks And Virtual Communities -- Bibliography -- Online Resources -- Index. Arno Scharl (ed.). Includes Bibliographical References (p. [259]-290) And Index. The Book Presents The Major Issues Of Ontological Engineering And Describes The Most Outstanding Ontologies That Are Currently Available. It Covers The Practical Aspects Of Selecting And Applying Methodologies, Languages And Tools For Building Ontologies. Ontological Engineering Will Be Of Great Value To Students And Researchers, And To Developers Who Want To Integrate Ontologies In Their Information Systems.--jacket. Theoretical Foundations Of Ontologies -- The Most Outstanding Ontologies -- Methodologies And Methods For Building Ontologies -- Languages For Building Ontologies -- Ontology Tools. Includes Bibliographical References (p. 363-388) And Index. This book brings together high quality articles exploring the design, implementation, management, funding, promotion and evaluation of networked information systems that advocate sustainability and the protection of natural ecosystems. Case studies of deployed and planned information systems complement theoretical work on the methodological, technological and organizational foundations of environmental online communication. Ontologies are widely used in Knowledge Engineering, Artificial Intelligence and Computer Science, in applications related to knowledge management, natural language processing, e-commerce, intelligent integration information, information retrieval, database design and integration, bio-informatics, education, and in new emerging fields like the Semantic Web. "Knowledge Discovery in Multiple Databases is suitable for researchers, professionals and students in data mining, distributed data analysis, and machine learning, who are interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might involve knowledge discovery in databases and data mining."--Jacket The twenty-first century marks the beginning of an era in which the traditional pillars of economic power - capital, land, materials and labor - are no longer the main determinants of business success; instead, achievement will be essentially determined by our ability to use knowledge, a precious global resource, wisely.