Computational Intelligence for Decision Support

Computational Intelligence for Decision Support

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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.The entity-relationship (ER) [Chen 1976] approach provides an effective way for conceptual modeling of data. (Actually ... Both entity sets and relationship sets can be described by attributes. ER modeling typically makes use of ER diagrams.


Title:Computational Intelligence for Decision Support
Author: Zhengxin Chen
Publisher:CRC Press - 1999-11-24
ISBN-13:

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