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Intelligent decision support relies on techniques from a variety of
disciplines, including artificial intelligence and database
management systems. Most of the existing literature neglects the
relationship between these disciplines. By integrating AI and DBMS,
Computational Intelligence for Decision Support produces what other
texts don't: an explanation of how to use AI and DBMS together to
achieve high-level decision making. Threading relevant disciplines
from both science and industry, the author approaches computational
intelligence as the science developed for decision support. The use
of computational intelligence for reasoning and DBMS for retrieval
brings about a more active role for computational intelligence in
decision support, and merges computational intelligence and DBMS.
The introductory chapter on technical aspects makes the material
accessible, with or without a decision support background. The
examples illustrate the large number of applications and an
annotated bibliography allows you to easily delve into subjects of
greater interest. The integrated perspective creates a book that
is, all at once, technical, comprehensible, and usable. Now, more
than ever, it is important for science and business workers to
creatively combine their knowledge to generate effective, fruitful
decision support. Computational Intelligence for Decision Support
makes this task manageable.
This book presents state-of-the-art data warehousing research and practice from an integrated business and computer science perspective - the first monograph to do so - and broadens the scope of data mining by discussing it in terms of data warehousing. The material, rooted in database management systems and artificial intelligence, brings the intelligent techniques associated with AI to the entire process of data warehousing, from preparing data and building data warehousing to analyzing data stored in the data warehouses using data mining.
Intelligent agents are one of the most promising business tools in
our information rich world. An intelligent agent consists of a
software system capable of performing intelligent tasks within a
dynamic and unpredictable environment. They can be characterised by
various attributes including: autonomous, adaptive, collaborative,
communicative, mobile, and reactive. Many problems are not well
defined and the information needed to make decisions is not
available. These problems are not easy to solve using conventional
computing approaches. Here, the intelligent agent paradigm may play
a major role in helping to solve these problems. This book, written
for application researchers, covers a broad selection of research
results that demonstrate, in an authoritative and clear manner, the
applications of agents within our information society.
criteria linear and nonlinear programming has proven to be a very
useful approach. * Knowledge management for enterprise: These
papers address various issues related to the application of
knowledge management in corporations using various techniques. A
particular emphasis here is on coordination and cooperation. * Risk
management: Better knowledge management also requires more advanced
techniques for risk management, to identify, control, and minimize
the impact of uncertain events, as shown in these papers, using
fuzzy set theory and other approaches for better risk management. *
Integration of data mining and knowledge management: As indicated
earlier, the integration of these two research fields is still in
the early stage. Nevertheless, as shown in the papers selected in
this volume, researchers have endearored to integrate data mining
methods such as neural networks with various aspects related to
knowledge management, such as decision support systems and expert
systems, for better knowledge management. September 2004 Yong Shi
Weixuan Xu Zhengxin Chen CASDMKM 2004 Organization Hosted by
Institute of Policy and Management at the Chinese Academy of
Sciences Graduate School of the Chinese Academy of Sciences
International Journal of Information Technology and Decision Making
Sponsored by Chinese Academy of Sciences National Natural Science
Foundation of China University of Nebraska at Omaha, USA Conference
Chairs Weixuan Xu, Chinese Academy of Sciences, China Yong Shi,
University of Nebraska at Omaha, USA Advisory Committee
Intelligent agents are one of the most promising business tools in
our information rich world. An intelligent agent consists of a
software system capable of performing intelligent tasks within a
dynamic and unpredictable environment. They can be characterised by
various attributes including: autonomous, adaptive, collaborative,
communicative, mobile, and reactive. Many problems are not well
defined and the information needed to make decisions is not
available. These problems are not easy to solve using conventional
computing approaches. Here, the intelligent agent paradigm may play
a major role in helping to solve these problems. This book, written
for application researchers, covers a broad selection of research
results that demonstrate, in an authoritative and clear manner, the
applications of agents within our information society.
Intelligent decision support relies on techniques from a variety of disciplines, including artificial intelligence and database management systems. Most of the existing literature neglects the relationship between these disciplines. By integrating AI and DBMS, Computational Intelligence for Decision Support produces what other texts don't: an explanation of how to use AI and DBMS together to achieve high-level decision making.
Threading relevant disciplines from both science and industry, the author approaches computational intelligence as the science developed for decision support. The use of computational intelligence for reasoning and DBMS for retrieval brings about a more active role for computational intelligence in decision support, and merges computational intelligence and DBMS. The introductory chapter on technical aspects makes the material accessible, with or without a decision support background. The examples illustrate the large number of applications and an annotated bibliography allows you to easily delve into subjects of greater interest.
The integrated perspective creates a book that is, all at once, technical, comprehensible, and usable. Now, more than ever, it is important for science and business workers to creatively combine their knowledge to generate effective, fruitful decision support. Computational Intelligence for Decision Support makes this task manageable.
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