• Information Systems Review
• /
• v.14 no.2
• /
• pp.1-19
• /
• 2012

Recently due to the enhancement of education and lifestyle, the trend of healthcare services are changed to a more active and differentiated service in which a continuous self health care is possible. The Smart-Telemedicine system offers medical services by merging Blue-tooth and telecommunication modules to former blood pressure, blood sugar, heartbeat and temperature measuring devices. Moreover, it could analyze one's health pattern which would be helpful for the patient to prevent potential future illness. In addition, the easier accesses to various remote controllable medical check-up programs are offered to public as a number of available smart phone are rapidly escalating. The Smart-Telemedicine system provides the most ideal interactive medical service via accessible smart phones and mobile medical check-up devices at anywhere and anytime. It is very beneficial since it can save patients' time and money because people can reach to the service right at their home and be allowed to take charge of their health care process via longitudinal health data. Therefore, not only social costs that occur in elderly community would be saved, but also business in various forms of medical service field transactions could be possible. This paper will suggest the Smart-Telemedicine System for preventive medicine, its design and analysis of business models and the evaluation of those model.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.1
• /
• pp.7-18
• /
• 2021

Recently, various connected industrial parks (CIPs) architectures using new technologies such as cloud computing, CPS, big data, fifth-generation mobile communication 5G, IIoT, VR-AR, and ventilation transportation AI algorithms have been proposed in Korea. Korea's small and medium-sized enterprises do not have the upper hand in technological competitiveness than overseas advanced countries such as the United States, Europe and Japan. For this reason, Korea's small and medium-sized enterprises have to invest a lot of money in technology research and development. As a latecomer, Korean SMEs need to improve their profitability in order to find sustainable growth potential. Financially, it is most efficient for small and medium-sized Korean companies to cut costs to increase their profitability. This paper made profitability improvement by reducing costs for small and medium-sized enterprises located in CIPs in Korea a major task. VJP (Vehicle Action Program) was noted as a way to reduce costs for small and medium-sized enterprises located in CIPs in Korea. The method of achieving minimum logistics costs for small businesses through the Korean CIPs payment system was analyzed. The details of the new Korean CIPs payment system were largely divided into four types: "Business", "Data", "Technique", and "Finance". Cost Benefit Analysis (CBA) was used as a performance analysis method for CIPs payment systems.

• Proceedings of the Korean DIstribution Association Conference
• /
• 2001.11b
• /
• pp.125-141
• /
• 2001

In this paper, the current problems of logistics industry in Korea and their possible solutions were discussed. With Korea Telecoms KT-Logis, the supplier and demander of logistics service would not have to invest large sum of money into their computer system. All they need is just a computer with internet connected. What KT-Logis influence on the logistics industry are the following; 1. Many logistics service supplier and demander can do the business on the web with one computer system. 2. This web based computer system does not only work on the office but also apply on the field worker such as delivery personnel or even the forwarder with mobile phone. 3. KT-Logis is an integrated system which cover the broad arrange of logistics management from truck management to customer relations management. 4. Finally, KT-Logis is web based systems which suits for current e-business and mobile environment. In future, more studies should be done to develop more progressive integrated logistics information systems with enterprise resource planning(ERP) and supply chain management(SCM).

• Journal of Intelligence and Information Systems
• /
• v.17 no.4
• /
• pp.109-130
• /
• 2011

One of the major problems in the area of data mining is the size of the data, as most data set has huge volume these days. Streams of data are normally accumulated into data storages or databases. Transactions in internet, mobile devices and ubiquitous environment produce streams of data continuously. Some data set are just buried un-used inside huge data storage due to its huge size. Some data set is quickly lost as soon as it is created as it is not saved due to many reasons. How to use this large size data and to use data on stream efficiently are challenging questions in the study of data mining. Stream data is a data set that is accumulated to the data storage from a data source continuously. The size of this data set, in many cases, becomes increasingly large over time. To mine information from this massive data, it takes too many resources such as storage, money and time. These unique characteristics of the stream data make it difficult and expensive to store all the stream data sets accumulated over time. Otherwise, if one uses only recent or partial of data to mine information or pattern, there can be losses of valuable information, which can be useful. To avoid these problems, this study suggests a method efficiently accumulates information or patterns in the form of rule set over time. A rule set is mined from a data set in stream and this rule set is accumulated into a master rule set storage, which is also a model for real-time decision making. One of the main advantages of this method is that it takes much smaller storage space compared to the traditional method, which saves the whole data set. Another advantage of using this method is that the accumulated rule set is used as a prediction model. Prompt response to the request from users is possible anytime as the rule set is ready anytime to be used to make decisions. This makes real-time decision making possible, which is the greatest advantage of this method. Based on theories of ensemble approaches, combination of many different models can produce better prediction model in performance. The consolidated rule set actually covers all the data set while the traditional sampling approach only covers part of the whole data set. This study uses a stock market data that has a heterogeneous data set as the characteristic of data varies over time. The indexes in stock market data can fluctuate in different situations whenever there is an event influencing the stock market index. Therefore the variance of the values in each variable is large compared to that of the homogeneous data set. Prediction with heterogeneous data set is naturally much more difficult, compared to that of homogeneous data set as it is more difficult to predict in unpredictable situation. This study tests two general mining approaches and compare prediction performances of these two suggested methods with the method we suggest in this study. The first approach is inducing a rule set from the recent data set to predict new data set. The seocnd one is inducing a rule set from all the data which have been accumulated from the beginning every time one has to predict new data set. We found neither of these two is as good as the method of accumulated rule set in its performance. Furthermore, the study shows experiments with different prediction models. The first approach is building a prediction model only with more important rule sets and the second approach is the method using all the rule sets by assigning weights on the rules based on their performance. The second approach shows better performance compared to the first one. The experiments also show that the suggested method in this study can be an efficient approach for mining information and pattern with stream data. This method has a limitation of bounding its application to stock market data. More dynamic real-time steam data set is desirable for the application of this method. There is also another problem in this study. When the number of rules is increasing over time, it has to manage special rules such as redundant rules or conflicting rules efficiently.