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1、英文原文Evolution of Decision Support System Architectures: applications for landplanning and management in CubaS. SegreraComputer Science Department, National Sugar Cane Research InstituteCarretera CAI Martnez Prieto km 2 , Ciudad de La Habana, CubaR. Ponce-HernndezEnvironmental and Resource Studies Pr
2、ogram/Department of Geography, Trent UniversityPeterborough, Ontario, CanadaandJ.ArciaComputer Science Department, National Sugar Cane Research InstituteCarretera CAI Martnez Prieto km 2 , Ciudad de La Habana, CubaABSTRACTThe main objective of this paper centers on reviewing the evolution of Decisio
3、n Support Systems (DSS) architectures, particularly as they apply to natural resources. Today it is difficult to conceive the existence of a rural planning automated system that doesnt include spatial analysis functionality and that does not consider the integrated use of different analytical module
4、s. This wider range of functions allow for solving problems from resource and environmental management. Geographical Information Systems (GIS), automated land evaluation, multi-criteria participatory analysis in decision making are but the most salient technologies in a DSS. DSS have evolved; their
5、architecture, mode of implementation, as well as their functionality and the incorporation of new computational techniques have advanced lately. In theparticular case of Cuba, the first steps in materializing this evolution have begun. At present, the National Sugar Cane Research Institute (INICA) l
6、eads a research project oriented towards the development and building of a dedicated DSS for sugar cane cropping. This is conceivedas an integrated SDSS (Spatial DSS) to support decision -making and multiple problem-solving in such a fundamental productive activity such as sugar caneagriculture in C
7、uba. Keywords: DSS, decision making, interoperability,Spatial Decision Support System. 1. INTRODUCTIONThere is a variety of definitions on the concept of Decision Support Systems (DSS) in published work. The DSS paradigm appeared at the end of the 70s. DSS were developed for applications in differen
8、t fields of specialization 16. Some authors like 17 considered a DSS as any computer system, which should support human decision-making. Most of the existent published works coincide in their understanding of DSS as tools to aid decision-making with problems that are not well structured. This type o
9、f analysis, demanding a recursive mechanism for iterations is the justification for using software and hardware that supports the search and selection of the most appropriate alternatives for the solution of existent problems (12, 10 and 14).It has been pointed out by 30 that decision support system
10、s should provide integration and regeneration of the information, support the exploratory nature of the scientific discovery process and allow the development of alternatives to apply information system technology, in order to increase the effectiveness of those responsible fordecisions, in situatio
11、ns where the computer can support and reinforce human judgement in the fulfillment of tasks,which have elements that cannot be specified beforehand.In these systems different modules are combined under a sole interface.It has been pointed out 3, 5, 21, 4, 33 and 8 that the paradigm of integration in
12、 DSS has opened up a wide range of analytical possibilities and practically limitless applications are established, by the use of models, simulations, statistical analysis, image processing, 3-D and temporal dimension visualization, together with the possibility of coding expert reasoning digitally.
13、 Also, software customization is possible specifically configured to required scales and applications, to achieve a flexible inter-operational environment. All those capabilities contribute to the integration of a powerful decision making tool for agriculture, where its use in natural resource prese
14、rvation and environmental management will have unquestionably profound practical benefits. The terms intelligent DSS or expert DSS have been introduced to make reference to the use of artificial intelligence techniques to extend the capacities of the DSS in the solution of problems (27 y 7). The tre
15、nd in the evolution of the DSS paradigm leaves clear that the focus on rational static analysis is being left in favour of a morei terative, complex and adaptative paradigm (25). When the spatial component is added to DSS, then spatial information management is possible evolving into Spatial Decisio
16、n Support Systems (SDSS).Six characteristics of DSS have been identified 5 and 13. These characteristics are: 1) explicit design to solve semi-structured problems; 2) powerful user interface and easy use; 3) ability to combine analytical models with data in a flexible way; 4) ability to explore the solution space building alternative; 5) capacity to support variety of styles in decision making; and 6) pr
