scholarly journals The Backbone of Decision Support Systems

Author(s):  
Philipp Hertweck ◽  
Jürgen Moßgraber ◽  
Efstratios Kontopoulos ◽  
Panagiotis Mitzias ◽  
Tobias Hellmund ◽  
...  

Understanding the current situation is critical in every natural disaster or crisis. Therefore, there is a need for accurate and up-to-date information about the scope, extent and impact of a disaster. The basis for this information is data that is available through a variety of sensors. Decision Support Systems (DSSs) support decision makers in disaster management, response, and recovery by providing early warnings, insights into the current situation and recommendations for mitigation actions. For this purpose, raw sensor data needs to be collected, analyzed, integrated, and its semantics need to be automatically understood by the system. This series of processes forms a generic sensor to decision chain. In this paper, we present solutions and technologies to integrate those steps seamlessly, also demonstrating how each step of the pipeline can be visualized.

Author(s):  
Philipp Hertweck ◽  
Jürgen Moßgraber ◽  
Efstratios Kontopoulos ◽  
Panagiotis Mitzias ◽  
Tobias Hellmund ◽  
...  

Understanding the current situation is critical in every natural disaster or crisis. Therefore, there is a need for accurate and up-to-date information about the scope, extent and impact of a disaster. The basis for this information is data that is available through a variety of sensors. Decision Support Systems (DSSs) support decision makers in disaster management, response, and recovery by providing early warnings, insights into the current situation and recommendations for mitigation actions. For this purpose, raw sensor data needs to be collected, analyzed, integrated, and its semantics need to be automatically understood by the system. This series of processes forms a generic sensor to decision chain. In this paper, we present solutions and technologies to integrate those steps seamlessly, also demonstrating how each step of the pipeline can be visualized.


Author(s):  
Zsolt T. Kardkovács

Whenever decision makers find out that they want to know more about how the business works and progresses, or why customers do what they do, then data miners are summoned, and business intelligence is to be built or altered. Data mining aims at retrieving valid, interesting, explicable connection between key factors for either operative reporting or supporting strategic planning. While data mining discovers static connections between factors, business intelligence visualizes relevant data for decision makers in order to make them identify fast changes and analyze precisely business states. In this chapter, the authors give a short introduction for data oriented decision support systems with data mining and business intelligence in it. While these techniques are widely used in business processes, there are much more bad practices than good ones. We try to make an attempt to demystify and clear the myths about these technologies, and determine who should and how (not) to use them.


Author(s):  
Vicki L. Sauter ◽  
Srikanth Mudigonda ◽  
Ashok Subramanian ◽  
Ray Creely

Increasingly, decision makers are incorporating large quantities of interrelated data in their decision making. Decision support systems need to provide visualization tools to help decision makers glean trends and patterns that will help them design and evaluate alternative actions. While visualization software that might be incorporated into decision support systems is available, the literature does not provide sufficient guidelines for selecting among possible visualizations or their attributes. This paper describes a case study of the development of a visualization component to represent regional relationship data. It addresses the specific information goals of the target organization, various constraints that needed to be satisfied, and how the goals were achieved via a suitable choice of visualization technology and visualization algorithms. The development process highlighted the need for specific visualizations to be driven by the specific problem characteristics as much as general rules of visualization. Lessons learned during the process and how these lessons may be generalized to address similar requirements is presented.


Author(s):  
David Paradice ◽  
Robert A. Davis

Decision support systems have always had a goal of supporting decision-makers. Over time, DSS have taken many forms, or many forms of computer-based support have been considered in the context of DSS, depending on one’s particular perspective. Regardless, there have been decision support systems (DSS), expert systems, executive information systems, group DSS (GDSS), group support systems (GSS), collaborative systems (or computer-supported collaborative work (CSCW) environments), knowledge-based systems, and inquiring systems, all of which are described elsewhere in this encyclopedia. The progression of decision support system types that have emerged follows to some degree the increasing complexity of the problems being addressed. Some of the early DSS involved single decision-makers utilizing spreadsheet models to solve problems. Such an approach would be inadequate in addressing complex problems because one aspect of problem complexity is that multiple stakeholders typically exist. Baldwin (1993) examined the need for supporting multiple views and provides the only attempt found in the information systems literature to operationalize the concept of a perspective. In his work, a view is defined as a set of beliefs that partially describe a general subject of discourse. He identified three major components of a view: the belief or notion to convey, a language to represent the notion, and a subject of discourse. He further described notions as comprising aspects and a vantage point. Aspects are the characteristics or attributes of a subject or situation that a particular notion emphasizes. A vantage point is described by the level of detail (i.e., overview or detailed analysis). Assuming the subject of discourse can be identified with the notion, Baldwin described how differences in views may occur via differences in the notion, the language, or both.


2019 ◽  
Vol 50 (4) ◽  
pp. 1020-1036 ◽  
Author(s):  
Verónica Ruiz-Ortiz ◽  
Santiago García-López ◽  
Abel Solera ◽  
Javier Paredes

Abstract The entry into force of Directive 2000/60/EC of the European Parliament and the Council of 23 October 2000 established a new model for the management and protection of surface water and groundwater in Europe. In this sense, a thorough knowledge of the basins is an essential step in achieving this European objective. The utility of integrative decision support systems (DSS) for decision-making in complex systems and multiple objectives allows decision-makers to identify characteristics and improve water management in a basin. In this research, hydrological and water management resource models have been combined, with the assistance of the DSS AQUATOOL, with the aim of deepening the consideration of losses by evaporation of reservoirs for a better design of the basin management rules. The case study treated is an Andalusian basin of the Atlantic zone (Spain). At the same time, different management strategies are analysed based on the optimization of the available resources by means of the conjunctive use of surface water and groundwater.


2017 ◽  
Author(s):  
Andysah Putera Utama Siahaan ◽  
Mesran Mesran

At the end of learning at an educational level, leaders often perceive difficulties in determining the best students at a certain level of education. Cumulative Achievement Index may not be used for decision-makers in determining the best students. There are criteria other criteria that influence them are actively organize, have never done a repair value, never follow short semester, never leave. Using these criteria and using Multi-Criteria Decision Making (MCDM) based methods applied to decision support systems can deliver the expected outcomes of higher education leaders. Many methods can be used on decision support systems such as Promethee, Promethee II, Electre, AHP, SAW, or TOPSIS. In this discussion, the author uses Extended Promethee II method in determining the best student at a college.


Author(s):  
Omar F. El-Gayar ◽  
Amit V. Deokar

Modern organizations are faced with numerous information management challenges in an increasingly complex and dynamic environment. Vast amounts of data and myriads of models of reality are routinely used to predict key outcomes. Decision support systems (DSS) play a key role in facilitating decision making through management of quantitative models, data, and interactive interfaces (Power, 2000). The basic thrust of such applications is to enable decision-makers to focus on making decisions rather than being heavily involved in gathering data and conceiving and selecting analytical decision models. Accordingly, the number and complexity of decision models and of modeling platforms has dramatically increased, rendering such models a corporate (and national) resource (Muhanna & Pick, 1994). Further, Internet technology has brought many new opportunities to conduct business electronically, leading to increased globalization. Managers and decision makers are increasingly collaborating in distributed environments in order to make efficient and effective use of organizational resources. Thus, the need for distributed decision support in general, and model sharing and reuse in particular, is greater today than ever before. This has attracted significant attention from researchers in information systems-related areas to develop a computing infrastructure to assist such distributed model management (Krishnan & Chari, 2000). In this article, we focus on distributed model management advances, and the discussion is organized as follows. The next section provides a background on model management systems from a life-cycle perspective. This is followed by a critical review of current research status on distributed decision support systems from a model management viewpoint with a particular emphasis on Web services. Future trends in this area are then discussed, followed by concluding remarks.


Author(s):  
David Paradice

While decision choices are certainly important and warrant appropriate attention, early stages of the decisionmaking process may be even more critical in terms of needing adequate support. The alternatives from which a decision maker may be able to choose are integrally tied to the assumptions made about the problem situation. Consequently, decision support systems (DSSs) may be more effective in helping decision makers to make good choices when support for problem formulation is provided. Research validates the notion that support for problem formulation and structuring leads to better decisions. This article explores this concept and looks at opportunities in emerging software trends to continue development of problem formulation support in DSS-type settings.


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