Применение систем поддержки принятия решений в ситуационных центрах
Decision support in equipment condition monitoring systems with image processing is analyzed. Long-run accumulation of information about earlier made decisions is used to realize the adaptiveness of the proposed approach. It is shown that unlike conventional classification problems, the recognition of abnormalities uses training samples supplemented with reward estimates of earlier decisions and can be tackled using reinforcement learning algorithms. We consider the basic stages of contextual multi-armed bandit algorithms during which the probabilistic distributions of each state are evaluated to evaluate the current knowledge of the states, and the decision space is explored to increase the decision-making efficiency. We propose a new decision-making method, which uses the probabilistic neural network to classify abnormal situation and the softmax rule to explore the decision space. A modelling experiment in image processing was carried out to show that our approach allows a higher accuracy of abnormality detection than other known methods, especially for small-size initial training samples.
Managing Intellectual Capital and Innovation for Sustainable and Inclusive Society: Proceedings of the MakeLearn and TIIM Joint International Conference 27–29 May 2015, Bari, Italy
In this work, in order of development of the previously proposed decision support system to counteract the development of infectious diseases (DSS «CDID») it is proposed evolutionary model (EM), that extends the capabilities of forecast – analytical studies on the spread of infectious disease processes for individual cities and areas of the country as a whole, as well as early assessment of ways solutions to the problems of prophylaxis and therapy in the study territories.
The article discusses the prospects of a decision support system to transport companies. The main characteristics of logistical BI-systems, proposed approaches its use to analyze the efficiency of the transport company.
An outline of a few methods in an emerging field of data analysis, “data interpretation”, is given as pertaining to medical informatics and being parts of a general interpretation issue. Specifically, the following subjects are covered: measuring correlation between categories, conceptual clustering, and generalization and interpretation of empirically derived concepts in taxonomies. It will be shown that all of these can be put as parts of the same inquiry.
The textbook focuses on information support of a decision making process: problem statement, typical stages, approaches to modeling of decision making conditions, as well as consequences of different alternatives selection. The role of expert estimates is examined. Such estimated are used for determining probabilities of problem situation, determining experts' coefficients of competency, forming estimates of the alternatives. The features of group decision making are considered. Special attention is paid to decision support on the strategic management level, in the conditions of changing and hardly predictable environment. Approaches to modeling of problem situations related with possible states of the environment in the future are examined in details. The history, current classification and perspectives of development of modern decision support systems, as well as their role in the integrated management information system are considered. In the textbook different decision support information systems (including ones developed in HSE Department of Business Analytics) are described. Description of the information systems are accomplished by the examples of their practical application. Business cases that are close to real decision making tasks are also examined. The textbook is compliant with current requirements of the Federal standard of higher education and may be useful for students, postgraduates and teachers working in the field of management, decision support and management information systems.
In modern world enterprises need to be agile in their operation and structure to react to changes quickly. One of the open questions here is how to develop the enterprise, or, to be more precise, if enterprise needs to be developed, and if yes, in which way. In this research we are focusing on the case when enterprise stakeholders understand the need of enterprise development, have ideas for that, and they need decision support method to understand if enterprise restructuring is likely to be successful and cost effective. Another covered topic is how to choose the best option for restructuring from variety provided. In this paper we describe the developed decision support method which combines DEMO methodology and transaction costs theory for quantitative costs estimation. To make this method applicable and reproducible we proposed few enhancements to DEMO notation.
In the paper an approach to identification of characteristics for assessment of IT strategic decisions is proposed. The main feature of the approach is associated with integration of Balanced Scorecard methodology for IT service (IT Balanced Scorecard) and COBIT standard. Such integration allows to describe a hierarchical structure of characteristics (metrics) for assessment of decisions efficiency in yje field of information technologies.