Download Decision Making Support Systems: Achievements, Trends and by Manuel Mora, Guisseppi A. Forgionne, Jatinder N. D. Gupta PDF

By Manuel Mora, Guisseppi A. Forgionne, Jatinder N. D. Gupta
Very good source spanning either historical past fabric and the way forward for selection help platforms. nice booklet for the practitioner and the theorist. - Gloria Phillips-Wren, Ph.D., Loyola university in Maryland, united states
Read Online or Download Decision Making Support Systems: Achievements, Trends and Challenges for the New Decade PDF
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Extra info for Decision Making Support Systems: Achievements, Trends and Challenges for the New Decade
Sample text
A decision maker might use the basic functionality of a GIS to identify areas liable to flooding along the banks of a river. This would provide information such as the area affected by a given rise in the water level or the new width of the river. This information could be the main input required for some types of decision and for this type of decision maker the GIS software might be said to be acting directly as a SDSS. Another user might wish to identify the sections of road affected by areas liable to flooding along the banks of a river.
Each DSS can have a specific or a very general purpose. Finally, each category of DSS can be deployed using a mainframe computer, a client/server LAN, or a Web-based architecture. In this expanded framework, DSS should also be categorized on these secondary dimensions. Most DSS are intra-organizational DSS designed for use by individuals in a company, but the rapid growth of the Internet is making it easier to implement interorganizational DSS targeted to customers or suppliers. General-purpose DSS software helps support broad tasks like project management and decision analysis, or it serves as a DSS generator to build a specific category of DSS.
Intelligent data warehousing and mining can support the data retrieval tasks, and it may be possible to adapt these methodologies for model and knowledge retrieval support. Differences in data, knowledge, and model structures, however, may necessitate the development of new methodologies for knowledge and model retrieval tasks. Integration offers an additional challenge. Currently, there are tools available to support some, but not all, of the integration tasks involved in DTS. Most of these tools rely on a linked approach, with several individual systems offering inputs for output processing by the others.