scholarly journals The Expert Knowledge Collection Methodology in the Decision Support System

2010 ◽  
Vol 2 (1) ◽  
Author(s):  
Anna Michalczyk ◽  
Tadeusz Krupa
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Expert Knowledge

Expert knowledge elicitation for decision-making in home energy retrofits

2014 ◽  
Vol 32 (5) ◽  
pp. 377-395 ◽  
Author(s):  
Daniel Yaw Addai Duah ◽  
Kevin Ford ◽  
Matt Syal
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Expert Knowledge ◽  
Knowledge Elicitation ◽  
Content Type ◽  
Intelligent Decision Support System ◽  
Intelligent Decision ◽  
Intelligent Decision Support

Purpose – The purpose of this paper is to develop a knowledge elicitation strategy to elicit and compile home energy retrofit knowledge that can be incorporated into the development of an intelligent decision support system to help increase the uptake of home energy retrofits. Major problems accounting for low adoption rates despite well-established benefits are: lack of information or information in unsuitable and usable format for decision making by homeowners. Despite the important role of expert knowledge in developing such systems, its elicitation has been fraught with challenges. Design/methodology/approach – Using extensive literature review and a Delphi-dominated data collection technique, the relevant knowledge of 19 industry experts, selected based on previously developed determinants of expert knowledge and suitable for decision making was elicited and compiled. Boolean logic was used to model and represent such knowledge for use as an intelligent decision support system. Findings – A combination of comprehensive knowledge elicitor training, Delphi technique, semi-structured interview, and job shadowing is a good elicitation strategy. It encourages experts to describe their knowledge in a natural way, relate to specific problems, and reduces bias. Relevant and consensus-based expert knowledge can be incorporated into the development of an intelligent decision support system. Research limitations/implications – The consensus-based and relevant expert knowledge can assist homeowners with decision making and industry practitioners and academia with corroboration and enhancement of existing knowledge. The strategy contributes to solving the knowledge elicitation challenge. Originality/value – No previous study regarding a knowledge elicitation strategy for developing an intelligent decision support system for the energy retrofit industry exists.

Fujitsu HIKARI, a Healthcare Decision Support System based on Biomedical Knowledge

2018 ◽  
Vol 6 (2) ◽  
pp. 26-49
Author(s):  
Boris Villazon-Terrazas ◽  
Nuria Garcia-Santa ◽  
Beatriz San Miguel ◽  
Angel del Rey-Mejías ◽  
Juan Carlos Muria ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Expert Knowledge ◽  
Care Quality ◽  
Medical Decision ◽  
Knowledge Graph ◽  
Theoretical Knowledge ◽  
Empirical Knowledge ◽  
Biomedical Knowledge

Fujitsu HIKARI is an artificial intelligence solution to assist clinicians in medical decision making, developed in the context of a joint collaboration project between Fujitsu Laboratories of Europe and Hospital Clínico San Carlos. This decision support system leverages on data analytics combined with healthcare semantic information to provide health estimations for patients, improving care quality and personalized treatment. Fujitsu HIKARI stands on the shoulders of biomedical knowledge, which includes (i) theoretical knowledge extracted from scientific literature, domain expert knowledge, and health standards; and (ii) empirical knowledge extracted from real patient electronic health records. The theoretical knowledge combines a theoretical knowledge graph (TKG) and a biomedical document repository (BDR). The empirical knowledge is encoded in an empirical knowledge graph (EKG). One of the main functionalities of Fujitsu HIKARI is the patient mental health risks assessment, which is based on the exploitation of its underlying Biomedical Knowledge.

scholarly journals Structure of the Decision Support System in the Development of Software Products

2021 ◽  
Vol 3 (4) ◽  
pp. 258-269
Author(s):  
Milena Shitova ◽  
Sergey Ovanesyan
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Expert Knowledge ◽  
Computing System ◽  
Point Of View ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Software Products ◽  
Software Product

This study examined the problems of calculating the time to create a software product using the example of the company "RKIT" LLC. The article discussed the analysis of the most effective, from the point of view of the authors, methods for assessing the complexity of projects. As a result of the review, the authors suggested to create a decision support system. This system will consist of two blocks: an automated information computing system based on the PERT method and an artificial intelligence system in the form of an expert system, which is a repository of expert knowledge. The creation of a DSS will reduce the time for experts to make decisions and reduce the likelihood of a decrease in the profitability of the project, which will lead to an increase in the company's profit.

Designing an algorithm for supporting information generation process optimization

2021 ◽  
Vol 10 (2) ◽  
pp. 901-906
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Process Optimization ◽  
Support System ◽  
Expert Knowledge ◽  
Organizational Systems ◽  
Generation Process ◽  
Structural Elements ◽  
Information Generation ◽  
Information Reference

The paper considers the structural elements for an automated information reference decision support system (AIRDSS) in hierarchical multilevel complex organizational systems (HMLCOS). The task to ensure the functioning of the AIRDSS has been formulated. To solve it the main steps have been designated, which comprise the calculation of the importance coefficients for the supporting information (SI) elements and ordering of options based on a decision-maker’s preference followed by the choice of the utility prospective one. On the basis of the steps considered, the authors propose the algorithm to form the optimal structure of the AIRDSS procedural component’ elements for obtaining SI; the algorithm has a number of advantages: calculation simplicity for various experiments, a relatively simple formalization of expert knowledge into numerical values of importance.

scholarly journals EXPERT AND KNOWLEDGE SYSTEMS AND DATABASES OF THE BEST PRACTICE

2004 ◽  
Vol 10 (3) ◽  
pp. 88-95 ◽  
Author(s):  
Artūras Kaklauskas ◽  
Edmundas Kazimieras Zavadskas ◽  
Leonarda Gargasaite
Keyword(s):  
Knowledge Management ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Best Practice ◽  
Expert Knowledge ◽  
Knowledge Bases ◽  
Knowledge Systems ◽  
Web Based ◽  
The Web

Investigations on the similarities and differences of expert, knowledge management and decision support systems are presented in the paper. Explicit and tacit knowledge is analysed. The benefit of knowledge management systems, their development and implementation are analysed. The aspects of the best practice and its knowledge bases and databases are described. Practical possibilities to apply the knowledge systems are presented. On the base of practice acquired during the FP 6 project INTELCITIES the database of the best practice and the web‐based decision support system for real estate are developed. On the base of the BPJTA in PuBs project the database of the best practice and the web‐based decision support system for retrofit of public buildings are developed.

scholarly journals Improving the Slum Planning Through Geospatial Decision Support System

2014 ◽  
Vol XL-2 ◽  
pp. 99-105 ◽  
Author(s):  
S. Shekhar
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Expert Knowledge ◽  
Local Level ◽  
Decision Makers ◽  
City Development ◽  
Political Processes ◽  
Formal Framework

In India, a number of schemes and programmes have been launched from time to time in order to promote integrated city development and to enable the slum dwellers to gain access to the basic services. Despite the use of geospatial technologies in planning, the local, state and central governments have only been partially successful in dealing with these problems. The study on existing policies and programmes also proved that when the government is the sole provider or mediator, GIS can become a tool of coercion rather than participatory decision-making. It has also been observed that local level administrators who have adopted Geospatial technology for local planning continue to base decision-making on existing political processes. In this juncture, geospatial decision support system (GSDSS) can provide a framework for integrating database management systems with analytical models, graphical display, tabular reporting capabilities and the expert knowledge of decision makers. This assists decision-makers to generate and evaluate alternative solutions to spatial problems. During this process, decision-makers undertake a process of decision research - producing a large number of possible decision alternatives and provide opportunities to involve the community in decision making. The objective is to help decision makers and planners to find solutions through a quantitative spatial evaluation and verification process. The study investigates the options for slum development in a formal framework of RAY (Rajiv Awas Yojana), an ambitious program of Indian Government for slum development. The software modules for realizing the GSDSS were developed using the ArcGIS and Community -VIZ software for Gulbarga city.

Fujitsu HIKARI, a Healthcare Decision Support System based on Biomedical Knowledge

2021 ◽  
pp. 568-593
Author(s):  
Boris Villazon-Terrazas ◽  
Nuria Garcia-Santa ◽  
Beatriz San Miguel ◽  
Angel del Rey-Mejías ◽  
Juan Carlos Muria ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Expert Knowledge ◽  
Care Quality ◽  
Knowledge Graph ◽  
Theoretical Knowledge ◽  
Empirical Knowledge ◽  
Biomedical Knowledge ◽  
Risks Assessment

Fujitsu HIKARI is an artificial intelligence solution to assist clinicians in medical decision making, developed in the context of a joint collaboration project between Fujitsu Laboratories of Europe and Hospital Clínico San Carlos. This decision support system leverages on data analytics combined with healthcare semantic information to provide health estimations for patients, improving care quality and personalized treatment. Fujitsu HIKARI stands on the shoulders of biomedical knowledge, which includes (i) theoretical knowledge extracted from scientific literature, domain expert knowledge, and health standards; and (ii) empirical knowledge extracted from real patient electronic health records. The theoretical knowledge combines a theoretical knowledge graph (TKG) and a biomedical document repository (BDR). The empirical knowledge is encoded in an empirical knowledge graph (EKG). One of the main functionalities of Fujitsu HIKARI is the patient mental health risks assessment, which is based on the exploitation of its underlying Biomedical Knowledge.

Contextual Fuzzy-Based Decision Support System Through Opinion Analysis: A Case Study at University of the Salerno

2016 ◽  
Vol 15 (05) ◽  
pp. 923-948 ◽  
Author(s):  
Carmen De Maio ◽  
Aurelio Tommasetti ◽  
Orlando Troisi ◽  
Massimiliano Vesci ◽  
Giuseppe Fenza ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Description Logic ◽  
Expert Knowledge ◽  
Business Strategies ◽  
Management Decision ◽  
Simulation Environment ◽  
Knowledge Based

According to the literature about customer satisfaction and loyalty, it is possible to define knowledge-based system to support management decision-making in the organizations. Nevertheless, the problem as to how much the context impacts on correlation has not been investigated in the literature. This paper focuses on developing of Decision Support System (DSS) taking into account correlations among statistical factors, i.e., expert knowledge, and customers’ opinions depending on several contextual features, e.g., culture, location, in order to build context-sensitive simulation environment. The proposed work defines a general system design workflow to tailor knowledge-based DSS by using a fuzzy model to quantify correlations among variables in a given context. We explore ontologies to represent correlations among statistical factors, e.g., Calculative Commitment, Quality of Service. We apply fuzzy data analysis techniques to train fuzzy classifier on the customer’s opinions collected by survey. Finally, synergistic usage of Description Logic and Fuzzy Theory allows the implementation of a simulation environment that supports the management team to tune business strategies. The framework has been instantiated for a case study to support public administration at the University of the Salerno.

scholarly journals A Clinical Decision Support System for the Diagnosis, Fracture Risks and Treatment of Osteoporosis

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Bjarni V. Halldorsson ◽  
Aron Hjalti Bjornsson ◽  
Haukur Tyr Gudmundsson ◽  
Elvar Orn Birgisson ◽  
Bjorn Runar Ludviksson ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Expert Knowledge ◽  
Treatment Options ◽  
Specific Treatment ◽  
Clinical Decision ◽  
Medical Decision

Expanding medical knowledge increases the potential risk of medical errors in clinical practice. We present, OPAD, a clinical decision support system in the field of the medical care of osteoporosis. We utilize clinical information from international guidelines and experts in the field of osteoporosis. Physicians are provided with user interface to insert standard patient data, from which OPAD provides instant diagnostic comments, 10-year risk of fragility fracture, treatment options for the given case, and when to offer a follow-up DXA-evaluation. Thus, the medical decision making is standardized according to the best expert knowledge at any given time. OPAD was evaluated in a set of 308 randomly selected individuals. OPAD’s ten-year fracture risk computation is nearly identical to FRAX (r= 0.988). In 58% of cases OPAD recommended DXA evaluation at the present time. Following a DXA measurement in all individuals, 71% of those that were recommended to have DXA at the present time received recommendation for further investigation or specific treatment by the OPAD. In only 5.9% of individuals in which DXA was not recommended, the result of the BMD measurement changed the recommendations given by OPAD.

An Ontology-Based Framework for Decision Support in Assembly Variant Design

2020 ◽  
Vol 21 (2) ◽  
Author(s):  
Shantanu Kumar Das ◽  
Abinash Kumar Swain
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Base ◽  
Support System ◽  
Expert Knowledge ◽  
Complex Problem ◽  
Variant Design ◽  
Knowledge Based ◽  
Core Concepts ◽  
Design Type

Abstract The designer generates a variant product by applying several design suggestions that fulfilled a variety of customer requirements. These design suggestions rely on multiple domains of expert knowledge, which are unstructured and implicit. Moreover, these design suggestions have an impact on assembly joint information (liaison), which makes the variant design a complex problem. To effectively support the designers, this work presents a knowledge-based decision support system for assembly variant design using ontology. First, a knowledge base is built by the development of an ontology to formally represent the taxonomy, properties, and causal relationships of/among core concepts involved in the variant design. Second, a five-step sequential procedure is established to facilitate the utilization of this knowledge base for decision-making in variant design. The procedure takes the extracted liaison information from the CAD model of an existing product as the input and further used for generating a set of variant design decisions as the output through Semantic Web Rule Language (SWRL) rule-based reasoning. The inferred outputs by the process of reasoning are the design suggestions, the variant design type required for each design suggestion, and its effect on joint information. Based on the evaluation of the ontology, the precision, recall, and F-measure obtained are 79.3%, 82.1%, and 80.67%, respectively. Finally, the efficacy of the knowledge-based decision support system is evaluated using case studies from the aerospace and automotive domain.

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