Dynamic Representation of a Situation: A Step of a Decision Support Process

2009 ◽  
Author(s):  
Fahem Kebair ◽  
Frédéric Serin
Keyword(s):  
Decision Support ◽  
Dynamic Representation ◽  
Decision Support Process

scholarly journals Knowledge Acquisition and Representation for High-Performance Building Design: A Review for Defining Requirements for Developing a Design Expert System

2021 ◽  
Vol 13 (9) ◽  
pp. 4640
Author(s):  
Seung-Yeoun Choi ◽  
Sean-Hay Kim
Keyword(s):  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Data Analytics ◽  
High Performance ◽  
Problem Definition ◽  
Design Expert ◽  
Decision Support Process ◽  
High Performance Building

New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.

ASSIGNMENT PROBLEM AS A DECISION SUPPORT PROCESS TOOLKIT IN THE LOGISTIC FIRM TURNOVER

2021 ◽  
Vol 2021 (2) ◽  
pp. 24-31
Author(s):  
Vladimir Kovalev ◽  
Elena Averchenkova ◽  
Andrey Averchenkov
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Assignment Problem ◽  
Problem Solution ◽  
Logistics Enterprise ◽  
Firm Turnover ◽  
Decision Support Process

The features of the decision-making at a logistics enterprise are shown. The assignment problem solution on the example of a specific commodity item made it possible to determine the directions of increasing the efficiency of the warehouse complex functioning, to propose the distribution of goods by cells, to find the optimal option for storing goods in the warehouse of a specific logistics enterprise.

scholarly journals New Patterns And Techniques In Knowledge Discovery Through Data Mining

2013 ◽  
Vol 7 (1) ◽  
pp. 947-954
Author(s):  
Tiruveedula Gopi Krishna ◽  
Dr.Mohamed Abdeldaiem Abdelhadi ◽  
M.Madhusudhana Subramanian
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Knowledge Discovery ◽  
Large Datasets ◽  
Automatic Process ◽  
Surface Complex ◽  
Structural Patterns ◽  
Very Large Datasets ◽  
Complex Relationships ◽  
Decision Support Process

The main focus of this paper discussion was on mining and its set of techniques used in an automated approach to exhaustively explore and bring to the surface complex relationships in very large datasets. We discussed how a decision support process can be used to search for patterns of information in data. And also discussed different techniques for finding and describing structural patterns in data as well. Knowledge Discovery is a concept that describes the process of automatically searching large volumes of data for patterns that can be considered knowledge about the data. We discussed all automatic and semi-automatic process of discovering the patterns in data that how it leads to some advantage in businesses to make knowledge driven decisions, which help the company to succeed and compete.

Assessment of wind damage risk in forest areas: application with GIS and Fuzzy-AHP MATLAB graphical user interface tool

2021 ◽  
Author(s):  
Meryem Tahri ◽  
Jan Kašpar ◽  
Miroslav Novotny ◽  
Haytham Tahri ◽  
Mohamed Maanan
Keyword(s):  
Decision Support ◽  
User Interface ◽  
Graphical User Interface ◽  
Fuzzy Ahp ◽  
Research Work ◽  
Forest Degradation ◽  
Geographical Information ◽  
Wind Damage ◽  
Decision Support Process ◽  
Damage Risk

<p>In the current situation, the forest degradation areas caused by severe wind-breaking has steadily risen. This research proposes an efficient decision support tool to reduce wind damage risk and monitor forest zones. This study provides an outcome of the role of the combination of geographical information system (GIS) and Fuzzy-AHP MATLAB graphical user interface (GUI) for forest managers and environmental consultants. The user-friendly application shows how the research work ensures forest spatial planning and monitoring on ecological and forest management purposes on a regional and national worldwide scale. A representative Czech case study was chosen regarding different parameter characteristics to test our approach. The study also used map surfaces from field survey sampling results and compared the ground truth values at specific locations with data from the new model. The GIS and Fuzzy-AHP GUI are helpful for various consultants in optimizing the decision-support process in many fields.</p>

scholarly journals A Knowledge Management and Decision Support Model for Enterprises

2011 ◽  
Vol 2011 ◽  
pp. 1-16 ◽  
Author(s):  
Patrizia Ribino ◽  
Agnese Augello ◽  
Giuseppe Lo Re ◽  
Salvatore Gaglio
Keyword(s):  
Knowledge Management ◽  
Decision Support ◽  
Planning Process ◽  
Semantic Space ◽  
Knowledge Bases ◽  
Data Driven ◽  
Knowledge Representation And Reasoning ◽  
Decision Support Model ◽  
Intelligent Search ◽  
Decision Support Process

We propose a novel knowledge management system (KMS) for enterprises. Our system exploits two different approaches for knowledge representation and reasoning: a document-based approach based on data-driven creation of a semantic space and an ontology-based model. Furthermore, we provide an expert system capable of supporting the enterprise decisional processes and a semantic engine which performs intelligent search on the enterprise knowledge bases. The decision support process exploits the Bayesian networks model to improve business planning process when performed under uncertainty.

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