• Information Systems Review
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• v.9 no.3
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• pp.1-32
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• 2007

Recently, Korean universities have experienced steady decline in enrollments in MIS majors, which raises a serious concern to both academia and business leaders as well. With roles of MIS in corporate worlds are expanding and demands for newer breeds of MIS graduates ever growing, this trend, also observed in the US, poses a puzzling yet interesting research agenda. To come to grips with the problem and to suggest a robust curricula model for the future, this paper approaches the problem from various angles. The model first examines perceptions on MIS of Korean students; it then delineates existing curricula models to identify core MIS courses. The compilation is then juxtaposed by MIS course information from major US and Korean colleges, leading to categorizing major MIS subfields. The paper then tries to identify as-is and desired status of MIS curriculum, based on inquiry results from both academia and IS practitioners. Together with career tracks concretely described in this paper, the model would serve to fill the perception gaps in and to meet the future goals for MIS education in Korea.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.147-157
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• 2022

Recently, many studies have been conducted to analyze traffic or object recognition that classifies vehicles through artificial intelligence-based prediction models using CCTV (Closed Circuit TeleVision)or drone images. In order to develop an object recognition deep learning model for accurate traffic estimation, systematic data construction is required, and related standardized guidelines are insufficient. In this study, previous studies were analyzed to derive guidelines for establishing artificial intelligence-based training data for traffic estimation using drone images, and business reports or training data for artificial intelligence and quality management guidelines were referenced. The guidelines for data construction are divided into data acquisition, preprocessing, and validation, and guidelines for notice and evaluation index for each item are presented. The guidelines for data construction aims to provide assistance in the development of a robust and generalized artificial intelligence model in analyzing the estimation of road traffic based on drone image artificial intelligence.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.203-214
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• 2012

As users of mobile devices such as smart phone are rapidly growing owing to the development of information technology, interest for information retrieval and a variety of services using mobile devices is gradually increasing. Users want to get the tourist information service through the use of mobile devices and accordingly, Korea local governments are trying to provide a variety of services on the mobile tourist information via smart phone. As more interest and requirements on mobile tourist information service, researches on types and preferences of mobile tourist information, measurement of the quality of service, the user's satisfaction and re-use is currently being done. However, meanwhile, the research on the content analysis classified and investigated a wide variety of numerical rating scale such as research topics of research papers, research methodology is wholly lacking. Thus, in terms of the research need on a systematic study of the domestic mobile tourist information, this study presented the research tendencies and implications of yearly research trends, research subjects, statistical analysis techniques, research methods, research models and theories related to the mobile tourist information focusing on journals listed on the National Research Foundation of Korea.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.2
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• pp.195-202
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• 2009

As the generation and application of spatial information is gradually expanded not only in traditional surveying fields but also a CNS and an ITS recently. The Accuracy and the newest of data grow to be an important element. But digital map is updated with system based tile. So, it is hard to get the newest of data and to be satisfied with user requirements. In this study, management system is developed to manage feature change efficiently using bid informations from NaraJangter which service the bid informations. A construction works with change possibility of feature from bid informations are classified and are made DB. And the DB is used as the feature change forecast informations. Also, It is converted from bid information of text form to positioning informations connected to spatial information data. If this system is made successfully, this system contributes to reduce the cost for the update of digital map and to take the newest date of spatial informations.

• Journal of the Korea Convergence Society
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• v.11 no.5
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• pp.9-17
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• 2020

Human activity recognition(HAR) using smartphones is a hot research topic in computational intelligence. Smartphones are equipped with a variety of sensors. Fusing the data of these sensors could enable applications to recognize a large number of activities. However, these devices have fewer resources because of the limited number of sensors available, and feature selection and classification methods are required to achieve optimal performance and efficient feature extraction. This paper proposes a smartphone-based HAR scheme according to these requirements. The proposed method in this paper extracts time-domain features from acceleration sensors, gyro sensors, and barometer sensors, and recognizes activities with high accuracy by applying KDA and SVM. This approach selects the most relevant feature of each sensor for each activity. Our comparison results shows that the proposed system outperforms previous smartphone-based HAR systems.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.663-671
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• 2020

Recently, there have been many natural disasters in Korea, not only in forest areas but also in urban areas, and the national requirements for them are increasing. In particular, there is no pre-disaster information system that can systematically manage the collapse of the slope of the national highway. In this study, big data analysis was conducted on the factors causing slope collapse based on the detailed investigation report on the slope collapse of national roads in Gangwon-do and Gyeongsang-do areas managed by the Cut Slope Management System (CSMS) and the basic survey of slope failures. Based on the analysis results, a slope collapse risk prediction model was established through Adaboost, a classification-based machine learning model, reflecting the collapse slope location and weather information. It also developed a visualization map for the risk of slope collapse, which is a visualization program, to show that it can be used for preemptive disaster prevention measures by identifying the risk of slope due to changes in weather conditions.

• Journal of Digital Convergence
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• v.12 no.7
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• pp.133-145
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• 2014

Recently, Hospital information systems have the large databases by wide range offices for hospital management, health care to improve the quality of care. However, hospital information systems for information security measures are insufficient. Therefore, when we construct the hospital information system, we have to audit the information security measures for them, and we have to manage the ISMS(Information Security Management System) to maintain the information protection level through the risk managements. In this paper, we suggested the hospital information security audit model for the protection of health information privacy by the current hospital information systems, information security management system(ISMS), and hospital information security requirements and threats. We derived the check items compared with ISO27799 reflected the characteristics of the hospital. We classified the security domains as the physical, technical, administrative domain, and derived the check items for information security. We also designed the check lists by mapping the ISO27799 risk management process to improve the security and efficiency simultaneously. Our model by the five-point scale survey of IT experts was verified the suitability with the average of 4.91 points.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.135-144
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• 2024

In this study, we propose an automatic classification model for quantitative multidimensional analysis based on facet theory to understand public opinions and demands on major issues through big data analysis. Civil complaints, as a form of public feedback, are generated by various individuals on multiple topics repeatedly and continuously in real-time, which can be challenging for officials to read and analyze efficiently. Specifically, our research introduces a new classification framework that utilizes facet theory and political analysis models to analyze the characteristics of citizen complaints and apply them to the policy-making process. Furthermore, to reduce administrative tasks related to complaint analysis and processing and to facilitate citizen policy participation, we employ deep learning to automatically extract and classify attributes based on the facet analysis framework. The results of this study are expected to provide important insights into understanding and analyzing the characteristics of big data related to citizen complaints, which can pave the way for future research in various fields beyond the public sector, such as education, industry, and healthcare, for quantifying unstructured data and utilizing multidimensional analysis. In practical terms, improving the processing system for large-scale electronic complaints and automation through deep learning can enhance the efficiency and responsiveness of complaint handling, and this approach can also be applied to text data processing in other fields.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.8
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• pp.249-258
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• 2022

Since the advent of Bitcoin, various virtual assets have been actively traded through virtual asset services of virtual asset exchanges. Recently, security accidents have frequently occurred in virtual asset exchanges, so the government is obligated to obtain information security management system (ISMS) certification to strengthen information protection of virtual asset exchanges, and 56 additional specialized items have been established. In this paper, we compared the domain importance of ISMS and CryptoCurrency Security Standard (CCSS) which is a set of requirements for all information systems that make use of cryptocurrencies, and analyzed the results after mapping them to gain insight into the characteristics of each certification system. Improvements for 4 items of High Level were derived by classifying the priorities for improvement items into 3 stages: High, Medium, and Low. These results can provide priority for virtual asset and information system security, support method and systematic decision-making on improvement of certified items, and contribute to vitalization of virtual asset transactions by enhancing the reliability and safety of virtual asset services.

• The Journal of Society for e-Business Studies
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• v.8 no.3
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• pp.177-195
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• 2003

In a traditional customer support environment, mainly call centers or service centers are responsible for receiving inquiries from their customers via telephone calls. Due to the rapid growth of Internet with its widespread acceptance and accessibility, means of communication with customers in the traditional customer support center, such as telephones, letters, and direct-visiting, have been replaced by e-mails and bulletin board systems (BBSs) using the Internet constantly. BBSs are basically question and answer systems, they require some lead time to get answer from administrator. To reduce lead time, BBSs enable remote customers or users to log on and tap into a knowledge database that is generally formatted in the form of Frequently Asked Questions (FAQs) that provide answers and solutions to the common problems. And, many different types of the questions are mixed on the BBS. It is a burden to administrator. To build FAQs and to support BBS adminstrator, a supporting tool which is to categorize questions is helpful. In this research, we suggest an interactive question categorizing methodology which consists of steps to present question using keywords, identifying keywords' affinity, computing similarity among questions, and clustering questions. This methodology allows users to interact iteratively for clear manifestation of ambiguous questions. We also developed a prototype system, IQC (interactive question categorizer) and evaluated its performance using the comparison experiments with other systems. IQC is not a general purposed system, but it produces a good result in a given specific domain.