• Asia pacific journal of information systems
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• v.11 no.2
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• pp.181-203
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• 2001

As the Internet is used extensively, multimedia information becomes more prevailing and accessible. However, legacy decision support systems rarely mention how to put such multimedia contents into practical use for decision making and support. In particular, no proposals have yet been made on how to integrate the decision technologies and multimedia databases in model management systems. Hence, the aim of this paper is to propose a new model management method to integrating decision technologies and an image database management system to create a multimedia decision support. For this purpose, extended ARG and structured modeling techniques are adopted, to represent image contents and mathematical models respectively. A web-based prototype system is presented to illustrate the feasibility and usability of the methodology.

• Nuclear Engineering and Technology
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• v.39 no.6
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• pp.703-716
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• 2007

As digital and computer technologies have grown, human-machine interfaces (HMIs) have evolved. In safety-critical systems, especially in nuclear power plants (NPPs), HMIs are important for reducing operational costs, the number of necessary operators, and the probability of accident occurrence. Efforts have been made to improve main control room (MCR) interface design and to develop automated or decision support systems to ensure convenient operation and maintenance. In this paper, an integrated decision support system to aid operator cognitive processes is proposed for advanced MCRs of future NPPs. This work suggests the design concept of a decision support system which accounts for an operator's cognitive processes. The proposed system supports not only a particular task, but also the entire operation process based on a human cognitive process model. In this paper, the operator's operation processes are analyzed according to a human cognitive process model and appropriate support systems that support each cognitive process activity are suggested.

• Asia pacific journal of information systems
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• v.14 no.1
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• pp.125-142
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• 2004

Knowledge management has been increasingly recognized as important in business management context. Although knowledge management has been proposed as an enabler to reach competitive advantage, little research has considered applying knowledge to business decision-making activities, which may be the main task of enterprise management. The application of knowledge to decision-making has a more significant impact on organizational performance than mere knowledge management for operational level processing. For this purpose, the present study proposes a decision support loop based on the integration of knowledge by adopting a cognitive modeling approach. The proposed model is then discussed, in the real context of an application case.

• The Journal of Information Systems
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• v.20 no.1
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• pp.107-126
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• 2011

Model-based decision support system (DSS) has acted as a crucial role in strengthening the business competitiveness by providing a way of modeling and solving real-world decision problems in a quantitative and scientific manner. It is even more important for trading OTC derivatives, which requires extensive financial-engineering expertise while actively reacting to the continuously changing financial market. This paper proposes a flexible model-based DSS architecture that can support user-friendly interface for executing and analyzing the models and can adapt to the changes of financial market seamlessly. For user-friendliness, we implement the user-interfaces (UIs) using Microsoft Excel, a very widely used spreadsheet program for its great generality and extensibility. Users can utilize the analysis results of DSS or reprocess them for their special needs through the UIs in the form of familiar spreadsheets easily. For adaptiveness to the markets, the proposed architecture is constructed based on the object-oriented concepts, which enables such changes as release of a new financial product can be updated into the system without any delay at the lowest cost. We investigate the practical benefits and limitations of the proposed architecture by a case study on the construction of Model-based Trading Support System (MTSS), performed by a commercial bank in Korea.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1496-1515
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• 2017

One of the most visible developments in Decision Support Systems (DSS) was the emergence of rule-based expert systems. Hence, despite their success in many sectors, developers of Medical Rule-Based Systems have met several critical problems. Firstly, the rules are related to a clearly stated subject. Secondly, a rule-based system can only learn by updating of its rule-base, since it requires explicit knowledge of the used domain. Solutions to these problems have been sought through improved techniques and tools, improved development paradigms, knowledge modeling languages and ontology, as well as advanced reasoning techniques such as case-based reasoning (CBR) which is well suited to provide decision support in the healthcare setting. However, using CBR reveals some drawbacks, mainly in its interrelated tasks: the retrieval and the adaptation. For the retrieval task, a major drawback raises when several similar cases are found and consequently several solutions. Hence, a choice for the best solution must be done. To overcome these limitations, numerous useful works related to the retrieval task were conducted with simple and convenient procedures or by combining CBR with other techniques. Through this paper, we provide a combining approach using the multi-criteria analysis (MCA) to help, the traditional retrieval task of CBR, in choosing the best solution. Afterwards, we integrate this approach in a decision model to support medical decision. We present, also, some preliminary results and suggestions to extend our approach.

• The Journal of Information Systems
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• v.8 no.2
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• pp.91-110
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• 1999

Recently, searching agent systems to help purchase of products between business and customer have been actively studied in Electronic Commerce(EC). However, the most of comparative searching agent systems are only provided customers with searching results by the keyword-based search, and is not support the efficient decision models to be selected products considering the customer's requirements. This paper proposes the decision agent system applied decision model as well as searching functions based on the keyword-input to be selected useful products in EC. The proposed decision agent system is consist of the user interface, provider interface, decision model. Especially, as the example of the decision model, this paper is designed and implemented the prototype of decision agent system which is normalized the searching data and value of customer's preference weight as to each attribute, and orderly provided customers with computed results. This agent system is also carried out sensitive analysis according to the reflection ratio of the each attribute.

• Journal of Korean Society of Rural Planning
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• v.15 no.4
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• pp.59-74
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• 2009

This study reviews and modifies general IS success models to find success factors of WIS(Web-based Information Systems) and to confirm the relationship between WIS success and user's satisfaction of web use. A WISSM(Web-based Information Success Model extended to include EQ(E-Quality) is developed to anticipate user's intention to use Web-based Agricultural Information System and fit into the survey data from 252 WIS users of RDA(Rural Development Administration). PLS is applied to estimate a structural model based on EQ-WISSM to test hypotheses including 1) users reach a high level of intention to use Web-based Information Systems when they feel a high level of interactivity among an 'E-Quality', 'Decision Making Support Satisfaction' and 'Task Support Satisfaction', and E-Quality boosts intention to use Web-based Information Systems. The results show high path coefficients and $R^2$ values and find followings; First, the EQ-WISSM explains the user's intention to use WAIS quite well. Second, E-Quality can be used well in web-based IS environment to predict IS Success. Finally, this research finds the importance of 'Task Support Satisfaction' as a mediator between 'Decision Making Support Satisfaction', 'E-Quality' and 'Intention to Use'.

• The Journal of Information Systems
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• v.29 no.1
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• pp.93-111
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• 2020

Purpose The purpose of this paper is to observe the relative priorities of importances among the modified versions of Block chain system, being based on AHP decision support model which should be also proposed in this paper. Design/methodology/approach Four versions modified from the beginning of Block chain were divided into Public& Permissionless, Private&Permissionless, Public&Permissioned and Private&Permissioned types. Five criteria for evaluating the four versions whether the version were suitable for Medical information system were introduced from five factors of Technologies Accept Model, which were Security, Availability, Variety, Reliability and Economical efficiency. We designed Decision support model based on AHP which would select the best alternative version suitable for introducing the Block chain technology into the medical information systems. We established the objective of the AHP model into finding the best choice among the four modified versions. First low layer of the model contains the five factors which consisted of Security, Availability, Variety, Reliability and Economical efficiency. Second low layer of the model contains the four modified versions which consisted Public&Permissionless, Private&Permissionless, Public&Permissioned and Private& Permissioned types. The structural questionnaire based on the AHP decision support model was designed and used to survey experts of medical areas. The collected data by the question investigation was analyzed by AHP analysis technique. Findings The importance priority of Security was highest among five factors of Technologies Accept Mode in the first layer. The importance priority of Private&Permissioned type was highest among four modified versions of Block chain technologies in second low layer. The second importance priority was Private&Permissionless type. The strong point of Private&Permissioned type is to be able to protect personal information and have faster processing speeds. The advantage of Private& Permissionless type is to be also able to protect personal information as well as from forging and altering transaction data. We recognized that it should be necessary to develop new Block chain technologies that would enable to have faster processing speeds as well as from forging and altering transaction data.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.117-124
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• 2019

Recently, medical IT solutions are being provided on a distributed environment basis. In Korea, the necessity of developing a clinical decision support system that can share medical information in a distributed environment has been recognized and studied. The existing clinical decision support system is being built using only medical information of its own within the hospital. This makes it difficult for existing systems to achieve good results in terms of efficiency and accuracy of decision support. In order to solve these limitations, this paper proposes a design and implementation method of clinical decision support system based on common data model in medical field. To explain the application process of the proposed model, we describe the development scenario of the clinical decision support system for the diagnosis of colorectal cancer. We also propose the essential requirements for the development of successful clinical decision support systems. Through this, it is expected that it will be possible to develop clinical decision support system that can be used in various hospitals and improve the efficiency and accuracy of the system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.