A new approach for load balancing in high performance decision support systems

AbstractDecision making in sport involves forecasting and selecting choices from different options of action, care, or management. These processes are conditioned by the available information (sometimes limited, fallible, or excessive), the cognitive limitations of the decision-maker (heuristics and biases), the finite amount of available time to make the decision, and the levels of risk and reward. Decision support systems have become increasingly common in sporting contexts such as scheduling optimization, skills evaluation and classification, decision-making assessment, talent identification and team selection, or injury risk assessment. However no specific, formalised framework exists to help guide either the development or evaluation of these systems. Drawing on a variety of literature, this paper proposes a decision support system development framework for specific use in high-performance sport. It proposes three separate criteria for this purpose: 1) Context Satisfaction, 2) Output Quality, and 3) Process Efficiency. Underpinning these criteria there are six specific components: Feasibility, Delivered knowledge, Decisional guidance, Data quality, System error, and System complexity. The proposed framework offers a systematic approach for users to ensure that each of the six components are considered and optimised before, during, and after developing the system. A DSS development framework for high-performance sport should help to improve both short and long term decision-making in a variety of sporting contexts.

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Scalable Decision Support Systems High Performance Data Warehousing

10.1007/springerreference_65695 ◽

2011 ◽

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

High Performance ◽

Support Systems ◽

Data Warehousing ◽

Performance Data

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A new combination rule for Spatial Decision Support Systems for epidemiology

International Journal of Health Geographics ◽

10.1186/s12942-019-0187-7 ◽

2019 ◽

Vol 18 (1) ◽

Author(s):

Luciana Moura Mendes de Lima ◽

Laísa Ribeiro de Sá ◽

Ana Flávia Uzeda dos Santos Macambira ◽

Jordana de Almeida Nogueira ◽

Rodrigo Pinheiro de Toledo Vianna ◽

...

Keyword(s):

Decision Making ◽

Decision Support ◽

Decision Support Systems ◽

Spatial Information ◽

Support Systems ◽

Final Decision ◽

Spatial Decision Support ◽

Spatial Decision Support Systems ◽

New Approach ◽

Priority Levels

Abstract Background Decision making in the health area usually involves several factors, options and data. In addition, it should take into account technological, social and spatial aspects, among others. Decision making methodologies need to address this set of information , and there is a small group of them with focus on epidemiological purposes, in particular Spatial Decision Support Systems (SDSS). Methods Makes uses a Multiple Criteria Decision Making (MCDM) method as a combining rule of results from a set of SDSS, where each one of them analyzes specific aspects of a complex problem. Specifically, each geo-object of the geographic region is processed, according to its own spatial information, by an SDSS using spatial and non-spatial data, inferential statistics and spatial and spatio-temporal analysis, which are then grouped together by a fuzzy rule-based system that will produce a georeferenced map. This means that, each SDSS provides an initial evaluation for each variable of the problem. The results are combined by the weighted linear combination (WLC) as a criterion in a MCDM problem, producing a final decision map about the priority levels for fight against a disease. In fact, the WLC works as a combining rule for those initial evaluations in a weighted manner, more than a MCDM, i.e., it combines those initial evaluations in order to build the final decision map. Results An example of using this new approach with real epidemiological data of tuberculosis in a Brazilian municipality is provided. As a result, the new approach provides a final map with four priority levels: “non-priority”, “non-priority tendency”, “priority tendency” and “priority”, for the fight against diseases. Conclusion The new approach may help public managers in the planning and direction of health actions, in the reorganization of public services, especially with regard to their levels of priorities.

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