Visual and multimedia tools are becoming more and more useful in transferring technical knowledge to decisionmakers and policymakers as well as to the public.
DSS plays an important role in water resources management. Guariso, Giorgio, and Werthner, H. Environmental Decision Support Systems.
Chichester, U. Loucks, Daniel P. Berlin, Germany: Springer-Verlag, Sprague Jr. Building Effective Decision Support Systems. A few examples of federal databases illustrate the types of applications afforded by computer and geospatial technologies. It is the most comprehensive database of its kind ever attempted anywhere in the world.
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The U. EPA's Watershed Atlas is a catalog of geospatial displays and analyses of information and data important for watershed protection and restoration. EPA's home webpage provides databases and software that apply to specific environmental media for example, water, air, land ; geographic information systems in support of mapping of environmental data; and models for predicting environmental impacts and increasing the level of understanding about natural systems and the way in which they react to varying conditions.
The EPA's Environmental Information Management System accesses descriptive information metadata for datasets, databases, documents, models, projects, and spatial data. Toggle navigation. Types of Data Water resources management requires a knowledge of the resources being managed.
Data Collection International organizations, governmental agencies, and private companies collect and store data to support local watershed and regional river basin management activities, as well as ongoing research on more global issues related to possible climate change impacts,. These data are needed to perform the assessments used in planning and decision-making devoted to various goals: Meeting agricultural, industrial, and municipal demands; Reducing waterborne diseases; Producing hydroelectric energy; Providing for increased navigation, recreation, and environmental protection; Coping with natural hazards of floods and droughts; and Restoring the aquatic and riparian ecosystems.
Long-Term Data. Large-Scale Data. Databases and Decision-Support Systems A considerable amount of data used in water resources assessments required for planning and management are now available from public agencies through the Internet.
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Decision-Support Systems. Bibliography Guariso, Giorgio, and Werthner, H. In order to make this decision, the effect of different variables including price on demand for the product and the subsequent profit must be evaluated. The manufacturer's perceptions of the demand for the product can be captured in a mathematical formula that portrays the relationship between profit, price, and other variables considered important. Once the relationships have been expressed, the decision maker may now want to change the values for different variables and see what the effect on profits would be.
The ability to save mathematical relationships and then obtain results for different values is a feature of many decision support systems. This is called "what-if" analysis, and today's spreadsheet software packages are fully equipped to support this decision-making activity. Of course, additional factors must be taken into consideration as well when making business decisions.
Hard-to-quantify factors such as future interest rates, new legislation, and hunches about product shelf life may all be considered. So even though the calculations may indicate that a certain demand for the product will be achieved at a certain price, the decision maker must use his or her judgment in making the final decision. If the decision maker simply follows the output of a process model, then the decision is being moved toward the structured end of the continuum. In certain corporate environments, it may be easier for the decision maker to follow the prescriptions of the DSS; users of support systems are usually aware of the risks associated with certain choices.
If decision makers feel that there is more risk associated with exercising judgment and opposing the suggestion of the DSS than there is in simply supporting the process, the DSS is moving the decision more toward the structured end of the spectrum.
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Therefore, the way in which a DSS will be used must be considered within the decision-making environment. There may be instances in which the decision maker has an idea of the knowledge that is desired, but not necessarily the best way to get that knowledge. This problem may be seen in the use of statistical analysis to support a decision.
Most statistical packages provide a variety of tests and will perform them on whatever data is presented, regardless of whether or not it is appropriate. This problem has been recognized by designers of support systems and has resulted in the development of DSS that support the choice of the type of analysis.
Carlson, John R. Carlson, and Lori L. Summer Chaudhry, Sohail S. January Gupta, Jatinder N. February Kimball, Ralph, and Kevin Strahlo. Laudon, Kenneth C. Macmillan, Muller-Boling, Detlef, and Susanne Kirchhoff. April Parkinson, Chris. Decision Shaping Systems. This community group shall advance the theory and practice of voter-centric design, empowering and equipping citizens.
This community group may draft suggestions and best practices and may coordinate with other groups to support pertinent standards. Voter decision support utilizes computing technology to provide decision support to voters, for instance to equip them during voting preparation. Voting preparation can start before an election season and can be described as an ongoing activity for some voters — voters, for instance, may gather information with respect to the ongoing performance of elected officials.
Approaches to empowering voters with computing technology should recognize that there may be a number of distinct voter preparation styles, information gathering styles, and information reviewing styles. A decision support system is a software system which supports individual or organizational decision-making activities. The history of decision support systems traces back to the middle of the s and the concepts and technologies are still evolving.
Historically, there are five types of decision support systems to consider: communication-driven, data-driven, document-driven, knowledge-driven and model-driven.
Data, Databases, and Decision-Support Systems
Communication-driven decision support systems provide users with the ability to communicate, collaborate and share knowledge with one another. Data-driven decision support systems provide users with the ability to access, manipulate and visualize data. Document-driven decision support systems provide users with the ability to search and retrieve documents and multimedia. Knowledge-driven decision support systems provide users with the ability to access and utilize structured knowledge, inference engines, reasoning systems, automated reasoning and expert systems.
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Model-driven decision support systems provide users with the ability to access and utilize statistical, financial, economic, optimization and simulation models. Voter decision support systems can draw upon a number of these approaches simultaneously.
cubosoft.net/libraries/map25.php An intelligent decision support system is a decision support system which makes extensive use of artificial intelligence techniques. Intelligent decision support systems have made use of expert systems. Expert systems are knowledge-based systems, software systems which reason and use knowledge bases to make decisions or solve complex problems.
In addition to uses in intelligent decision support systems, expert systems have been utilized in the applications of: interpretation, prediction, diagnosis, design, planning, monitoring, debugging, repair, instruction and control.