• Information Systems Review
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• v.25 no.4
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• pp.233-248
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• 2023

The increasing integration of intelligent information technologies within organizational systems has amplified the risk to personal information security. This escalation, in turn, has fueled growing apprehension about an organization's capabilities in safeguarding user data. While Internet users adopt a multifaceted approach in assessing a company's information security, existing research on the multiple dimensions of information security is decidedly sparse. Moreover, there is a conspicuous gap in investigations exploring whether users' evaluations of organizational information security differ across industry types. With an aim to bridge these gaps, our study strives to identify which information security attributes users perceive as most critical and to delve deeper into potential variations in these attributes across different industry sectors. To this end, we conducted a structured survey involving 498 users and utilized the analytic hierarchy process (AHP) to determine the relative significance of various information security attributes. Our results indicate that users place the greatest importance on the technological dimension of information security, followed closely by transparency. In the technological arena, banks and domestic portal providers earned high ratings, while for transparency, banks and governmental agencies stood out. Contrarily, social media providers received the lowest evaluations in both domains. By introducing a multidimensional model of information security attributes and highlighting the relative importance of each in the realm of information security research, this study provides a significant theoretical contribution. Moreover, the practical implications are noteworthy: our findings serve as a foundational resource for Internet service companies to discern the security attributes that demand their attention, thereby facilitating an enhancement of their information security measures.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.1001-1006
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• 2023

Globally, rising energy costs and increased energy demand are important issues for the food processing and manufacturing industries, which consume significant amounts of energy throughout the supply chain. Accordingly, there is a need for the development of a real-time energy monitoring and analysis system that can optimize energy use. In this study, a food factory energy monitoring system was proposed based on IoT installed in a food factory, including monitoring of each facility, energy supply and usage monitoring for the heat treatment process, and search functions. The system is based on the IoT sensor of the food processing plant and consists of PLC, database server, OPC-UA server, UI server, API server, and CIMON's HMI. The proposed system builds big data for food factories and provides facility-specific monitoring through collection functions, as well as energy supply and usage monitoring and search service functions for the heat treatment process. This data collection-based energy monitoring system will serve as a guide for the development of a small and medium-sized factory energy monitoring and management system for energy savings. In the future, this system can be used to identify and analyze energy usage to create quantitative energy saving measures that optimize process work.

• Journal of Cadastre & Land InformatiX
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• v.53 no.2
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• pp.67-77
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• 2023

As cities become overcrowded and concentrated, the demand for public services continues to increase due to the improvement of the living standards of urban residents. Among them, fire service can be seen as one of the important public services by reducing damage caused by accidents in emergency situations and affecting the improvement of access to medical services for urban residents. Rapid movement of patients and medical institutions within golden time and proper first aid are essential elements in emergency situations, and Seoul is a super-large city with a large population of about 10 million people and has a large number of emergency medical patients. Therefore, this study used spatial regression analysis to examine the factors affecting the delay factors of emergency dispatch in Seoul to secure golden time, and derived management priorities, and suggested implications for the management of emergency vehicle dispatch delay factors. As a result of the main analysis, land-use characteristics were the most influential factor in emergency vehicle dispatch time, and land-use mixing, commercial area density, average patient age, and average road length were found to affect emergency vehicle dispatch time in order. This study can be used as important basic data for an accurate understanding of the delay factors for emergency dispatch and preparing countermeasures according to priorities.

• Journal of Korean Society of Archives and Records Management
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• v.24 no.1
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• pp.205-230
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• 2024

Recently, as user demand to receive various information through one integrated institution has increased, "Larchiveum," which integrates the functions and services of archives, libraries, and museums, has been established. Thus, web information services are provided in an integrated manner through the Larchiveum website. This study attempted to analyze the information services on the Larchiveum website in detail. To this end, the researchers developed a web information service evaluation index reflecting the characteristics of Larchiveum that are differentiated from information services offered by websites of general archives, libraries, and museums. Recognizing the importance of evaluation indicators, the researchers developed evaluation indicators, and an evaluation of the three institutions' websites was conducted. The assessment showed that the currently operating Larchiveum website provides ample basic business introduction and interface navigation, but the use of search results in the information search area was insufficient. Complementary points were presented in these areas, and measures that would be effective if additionally operated were also suggested. This research sought to provide practical assistance in configuring and providing web services for the newly established Larchiveum in hopes that the evaluation indicators used in this study will be applied, supplemented, and utilized well in the future.

• Information Systems Review
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• v.25 no.1
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• pp.27-46
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• 2023

Research on restaurant recommender systems has been proposed due to the development of the food service industry and the increasing demand for restaurants. Existing restaurant recommendation studies extracted consumer preference information through quantitative information or online review sensitivity analysis, but there is a limitation that it cannot reflect consumer semantic preference information. In addition, there is a lack of recommendation research that reflects the detailed attributes of restaurants. To solve this problem, this study proposed a model that can learn the interaction between consumer preferences and restaurant attributes by applying deep learning techniques. First, the convolutional neural network was applied to online reviews to extract semantic preference information from consumers, and embedded techniques were applied to restaurant information to extract detailed attributes of restaurants. Finally, the interaction between consumer preference and restaurant attributes was learned through the element-wise products to predict the consumer preference rating. Experiments using an online review of Yelp.com to evaluate the performance of the proposed model in this study confirmed that the proposed model in this study showed excellent recommendation performance. By proposing a customized restaurant recommendation system using big data from the restaurant industry, this study expects to provide various academic and practical implications.

• The Journal of Bigdata
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• v.8 no.2
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• pp.1-13
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• 2023

Current analysis of air passengers mainly relies on statistical methods, but there are limitations in analyzing detailed aspects such as travel routes, number of regional passengers and airport access times. However, with the advancement of big data technology and revised three data acts, big data-based transportation analysis has become more active. Mobile communication data, which can precisely track the location of mobile phone terminals, can serve as valuable analytical data for transportation analysis. In this paper, we propose a air passenger Origin/Destination (O/D) extraction algorithm based on mobile communication data that overcomes the limitations of existing air transportation user analysis methods. The algorithm involves setting airport signal detection zones at each airport and extracting air passenger based on their base station connection history within these zones. By analyzing the base station connection data along the passenger's origin-destination paths, we estimate the entire travel route. For this paper, we extracted O/D information for both domestic and international air passengers at all domestic airports from January 2019 to December 2020. To compensate for errors caused by mobile communication service provider market shares, we applied a adjustment to correct the travel volume at a nationwide citizen level. Furthermore correlation analysis was performed on O/D data and aviation statistics data for air traffic users based on mobile communication data to verify the extracted data. Through this, there is a difference in the total amount (4.1 for domestic and 4.6 for international), but the correlation is high at 0.99, which is judged to be useful. The proposed algorithm in this paper enables a comprehensive and detailed analysis of air transportation users' travel behavior, regional/age group ratios, and can be utilized in various fields such as formulating airport-related policies and conducting regional market analysis.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.1-23
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• 2014

Market forecasting aims to estimate the sales volume of a product or service that is sold to consumers for a specific selling period. From the perspective of the enterprise, accurate market forecasting assists in determining the timing of new product introduction, product design, and establishing production plans and marketing strategies that enable a more efficient decision-making process. Moreover, accurate market forecasting enables governments to efficiently establish a national budget organization. This study aims to generate a market growth curve for ICT (information and communication technology) goods using past time series data; categorize products showing similar growth patterns; understand markets in the industry; and forecast the future outlook of such products. This study suggests the useful and meaningful process (or methodology) to identify the market growth pattern with quantitative growth model and data mining algorithm. The study employs the following methodology. At the first stage, past time series data are collected based on the target products or services of categorized industry. The data, such as the volume of sales and domestic consumption for a specific product or service, are collected from the relevant government ministry, the National Statistical Office, and other relevant government organizations. For collected data that may not be analyzed due to the lack of past data and the alteration of code names, data pre-processing work should be performed. At the second stage of this process, an optimal model for market forecasting should be selected. This model can be varied on the basis of the characteristics of each categorized industry. As this study is focused on the ICT industry, which has more frequent new technology appearances resulting in changes of the market structure, Logistic model, Gompertz model, and Bass model are selected. A hybrid model that combines different models can also be considered. The hybrid model considered for use in this study analyzes the size of the market potential through the Logistic and Gompertz models, and then the figures are used for the Bass model. The third stage of this process is to evaluate which model most accurately explains the data. In order to do this, the parameter should be estimated on the basis of the collected past time series data to generate the models' predictive value and calculate the root-mean squared error (RMSE). The model that shows the lowest average RMSE value for every product type is considered as the best model. At the fourth stage of this process, based on the estimated parameter value generated by the best model, a market growth pattern map is constructed with self-organizing map algorithm. A self-organizing map is learning with market pattern parameters for all products or services as input data, and the products or services are organized into an $N{\times}N$ map. The number of clusters increase from 2 to M, depending on the characteristics of the nodes on the map. The clusters are divided into zones, and the clusters with the ability to provide the most meaningful explanation are selected. Based on the final selection of clusters, the boundaries between the nodes are selected and, ultimately, the market growth pattern map is completed. The last step is to determine the final characteristics of the clusters as well as the market growth curve. The average of the market growth pattern parameters in the clusters is taken to be a representative figure. Using this figure, a growth curve is drawn for each cluster, and their characteristics are analyzed. Also, taking into consideration the product types in each cluster, their characteristics can be qualitatively generated. We expect that the process and system that this paper suggests can be used as a tool for forecasting demand in the ICT and other industries.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.3
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• pp.1-13
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• 2018

Landscape Architecture is a minority manpower field that requires wide knowledge and experience. Therefore, the service market is narrower than other fields, and education service for practitioners is lacking. The purpose of this study is to propose e-learning content development methodology that can provide customized landscaping practical education according to the level of education and increase the economic efficiency of the development process. First, in theoretical review, the ADDIE model was modified to select the curriculum development model that pursues efficiency and introduced the concept of reusing learning objects in the SCORM-based model. In particular, to overcome the problems presented in the precious studies, the analysis and design stages have been strengthened and faculty designers with integrated knowledge of Landscape Architecture and ICT have led the overall phase. The actual development process is based on a step by step procedure--analysis of landscaping practitioners needs and environments, etc., teaching and learning procedures and the design of activities considering contents reuse, the first development such as actual shooting and editing, and the second development reusing the first development content--and was done in the order of evaluation and revision of professionalism and satisfaction. As a result of the study, the space-based courses composed of modular learning objects were first developed as 216 courses in 8 subjects, as 208 courses in 3 subjects in total, in which the modularized learning object are crossed and combined in units and difficulty-based courses were second developed in 216 courses with 3 subjects in total. As a result of the evaluation the satisfaction assessment of the overall satisfaction was 4.20 and the average value of the eight measures was 3.97, both being close to 4.0. For the professional assessment, the scores of 8 subjects were very high at 84.8 to 96.4 points. in context, the scores of 5 subjects were equal to from 89.9 to 96.4 points. In conclusion, as the study was conducted based on a clear understanding of the digital characteristics of e-learning contents and general characteristic of the landscaping industry, it was possible to develop a curriculum by developing a course composed of modular learning objects and reusing learning objects by unit. In particular, it has been proven to be effective in conveying professional knowledge and experiences via general procedures and provided an opportunity to overcome some analog problems that may occur in offline education. In the future, further studies need to be done by expanding the content and by focusing on segmented subjects.

• Journal of Korean Academy of Nursing
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• v.28 no.4
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• pp.958-969
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• 1998

In this study, we attempted to investigate the needs and problems of the terminal cancer patients and their family caregivers to provide them with nursing information to improve their quality of life and prepare for a peaceful death. Data was collected from August 1, 1995 to July 31, 1996 at the internal medicine unit of S hospital in Seoul area with the two groups of participants who were family members of terminal cancer patients seventy four of them were in-patients and 34 were out-patients who were discharged from the same hospital for home care. The research tool used in this study has been developed by selecting the questionnaires from various references, modifying them for our purpose and refining them based on the results of preliminary study. While general background information about the patients was obtained by reviewing their medical records, all other information was collected by interviewing the primary family caregivers of the patients using the questionnaire. The data collected were analyzed with the SPSS PC/sup +/ program. The results of this study are summarized as follows ; 1) Most frequently complained symptoms of the terminal cancer patients were in the order of pain(87%), weakness(86.1%), anorexia(83.3%) and fatigue (80.6%). 2) Main therapies for the terminal cancer patients were pain control (58.3%), hyperalimentation(47.2%) and antibiotics(21.3%). 3) Special medical devices that terminal cancer patients used most were oxygen device (11.1%), and feeding tube(5.6%). Other devices were used by less than 5% of the patients. 4) The mobility of 70.4% of the patients was worse than ECOG 3 level, they had to stay in bed more than 50% of a day. 5) Patients wanted their medical staffs to help relieve pain(45.4%), various physical symptoms(29.6%), and problems associated with their emotion(11.1%). 6) 16.7% of the family caregivers hoped for full recovery of the patients, refusing to admit the status of the patients. Also, 37% wished for the extension of the patient's life at least for 6 months. 7) Only 38.9% of the family members was preparing for the patient's funeral. 8) 45.4% of family caregivers prefer hospital as the place for the patient's death, 39.8% their own home, and 14.8% undetermined. 9) Caregivers of the patients were mostly close family members, i.e., spouse(62%), and sons and daughters or daughter-in-laws(21.3%). 10) 43.5% of the family caregivers were aware of hospice care. 46.8% of them learned about the hospice care from the mass media, 27.7% from health professionals, and the rest from books and other sources. 11) Caregivers were asked about the most difficult problems they encounter in home care, 41 of them pointed out the lack of health professionals they can contact, counsel and get help from in case of emergency, 17 identified the difficulty of finding appropriate transportation to hospital, and 13 stated the difficulty of admission in hospital as needed. 12) 93.6% of family caregivers demanded 24-hour hot line, 80% the visiting nurses and doctors, and 69.4% the volunteer's help. The above results indicate that terminal patients and their family caregivers demand help from qualified health professionals whenever necessary. Hospice care system led by well-trained medical and nursing staffs is one of the viable answers for such demands.

• Korean Journal of Environment and Ecology
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• v.33 no.6
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• pp.624-635
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• 2019

Bird strike accidents, a collision between aircraft and birds, have been increasing annually due to an increasing number of aircraft operating each year to meet heavier demand for air traffic. As such, many airports have conducted studies to assess and manage bird strike risks effectively by identifying and ranking bird species that can damage aircraft based on the bird strike records. This study was intended to investigate the bird species that were likely to threaten aircraft and compare and discuss the risk of each species estimated by the single-matrix and multi-matrix risk assessment models based on the Integrated Flight Information Service (IFIS) data collected in Gimpo, Gimhae and Jeju Airports in South Korea from 2005 to 2013. We found that there was a difference in the assessment results between the two models. The single-matrix model estimated 2 species and 6 taxa in Gimpo and Gimhae Airports and 2 species and 5 taxa in Jeju Airport to have the risk score above "high," whereas the multi-matrix model estimated 3 species and 5 taxa in Gimpo Airport, 4 species and 5 taxa in Gimhae Airport, and 2 species and 3 taxa in Jeju Airport to have the risk score above "very high." Although both models estimated the similar high-risk species in Gimpo and Gimhae Airports, there was a significant difference in Jeju Airport. Gimpo and Gimhae Airports are near the estuary of a river, which is an excellent habitat for large and heavy waterbirds. On the other hand, Jeju Airport is near the coast and the city center, and small and light bird species are mostly observed. Since collisions with such species have little effect on aircraft fuselage, the impact of common variables between the two models was small, and the additional variables caused a significant difference between the estimation by the two models.