Decision Support Systems in the 21st CenturyPrentice Hall, 2003 - 611 страница For a Decision Support System course offered in business schools. Packed with essential information, this valuable text helps future business management professionals learn to make and support managerial decisions, providing a thorough understanding of the support aspect of DSS. Written from a cognitive processes and decision-making perspective, it concentrates on issues that emphasize managerial applications and the implication of decision support technology on those issues. - NEW - Intelligent software agents chapter added. - NEW - DSS system development chapter added - Provides logical sequencing of all DSS topics ranging from application types through design and deployment. - NEW - Building data warehouses chapter added. - NEW - Megaputers PolyAnalyst and TextAnalyst data mining and visualization software applications - Including detailed tutorials, data sets, and sample problems. - Strong managerial application and use approach. - Processes involved in making creative decisions and effectively solving problems. - Future of decision support systems - i.e. expert systems, artificial intelligence systems, and executive information systems. - Decision-making theory - e.g. decision |
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Introduction to Decision Support Systems | 2 |
Decisions and Decision Makers | 32 |
Decisions in the Organization | 85 |
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Чести термини и фразе
activities agent algorithms allow analysis approach architecture associated basic Chapter characteristics classification cognitive common complex concept contains creativity customers data mining data warehouse data warehousing database deci decision maker decision style Decision Support Systems decision tree decision-making determine domain DSS design DSS development DSSS elements end users environment example executive executive information systems expert system Figure focus focuses function fuzzy logic genetic algorithms groupware heuristics human identified implementation individual information systems input integration intelligent interaction interface issues knowledge base learning machine learning market basket analysis ment metadata method multiple neurode objective operational organization organizational outcome output participants percent performance probability problem context problem-solving require rules sion software agents solution solving sources specific stored strategy structure success Table task techniques tion types typical uncertainty understanding various