• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.23 no.4
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• pp.204-217
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• 2018

This study proposes a cost-effective method to monitor coastal environments using e-Navigation-implemented domestic and international ferries, and to analyze big data of records such as wind, temperature, salinity, waves, and currents that are gathered through e-Navigation system. First, we present the concept and architecture of e-Navigation operation system based on the General Information Center on Maritime Safety and Security. Then, the marine observation system that can be applied to ferries operating in our nation's territory is discussed. Analytical methods, such as spatio-temporal mixed effects model, ensemble method, and meshfree method, in handling real-time big data obtained by the e-Navigation observing system are then explained in detail. This study will support the implementation of the Korean e-Navigation project that focuses on the safety of small vessels such as coasters and fishing vessels.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.173-178
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• 2019

Recently, the types and amount of data generated, collected, and measured in IoT such as smart home, security, and factory are increasing. The technologies for IoT service include sensor devices to measure desired data, embedded software to control the devices such as signal processing, wireless network protocol to transmit and receive the measured data, and big data and AI-based analysis. In this paper, we focused on developing a gateway for interfacing heterogeneous sensor network protocols that are used in various IoT devices and propose a heterogeneous network interface IoT gateway. We utilized a OpenWrt-based wireless routers and used 6LoWAN stack for IP-based communication via BLE and IEEE 802.15.4 adapters. We developed a software to convert Z-Wave and LoRa packets into IP packet using our Python-based middleware. We expect the IoT gateway to be used as an effective device for collecting IoT big data.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.2
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• pp.377-388
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• 2019

The system proposed in this paper was suggested as a big data system that works in the age of artificial intelligence of the 4th Industrial Revolution. The proposed system can be a good example in terms of government 's development of new intelligent big data information system. For example, the proposed system may be introduced into the system of a department as a function of the integration of existing cinema ticket integration network or its networking. For this purpose, the proposed system transmits the user's profile to the film producer or other company, where it is provided as comparison data. Soon, the information is sent to the user-specific characteristic data and then the film-maker will be able to gauge the success of the three elements of the movie's performance, cinematic quality, and break-even point in real time, which are revealed through the movie review that the actual user feels, including the so-called 'new reinterpretation.

• Journal of the Korea Convergence Society
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• v.12 no.8
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• pp.13-21
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• 2021

This study aims to analyze tourist inflow trends and consumption patterns using a geographic information system and big data analysis system. Songdo Central Park and Chinatown were selected among the major tourist destinations in Incheon, and floating population analysis and card sales analysis were conducted for one month in June 2017. The number of tourists visiting Songdo Central Park from metropolitan cities across the country was highest in the order of Incheon Metropolitan City, Gyeonggi-do, and Seoul Metropolitan City, and the proportion of foreign tourists was the highest in China. The number of card consumption used by Chinatown tourists was 12.4% higher for men than for women, and the amount of card consumption was also higher for men by 18%. This study has implications for proposing a strategic plan for tourism policy by analyzing the inflow trend and consumption pattern of tourists and deriving major issues in the establishment of tourism policy. Based on this study, it is expected that it can be helpful in improving the construction of tourism infrastructure in the future.

• Journal of People, Plants, and Environment
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• v.23 no.2
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• pp.233-243
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• 2020

This study collects social big data used in various fields in the past 9 years and explains the patterns of major keywords of the arboretums and botanical gardens to use as the basic data to establish operational strategies for future arboretums and botanical gardens. A total of 6,245,278 cases of data were collected: 4,250,583 from blogs (68.1%), 1,843,677 from online cafes (29.5%), and 151,018 from knowledge search engine (2.4%). As a result of refining valid data, 1,223,162 cases were selected for analysis. We came up with keywords through big data, and used big data program Textom to derive keywords of arboretums and botanical gardens using text mining analysis. As a result, we identified keywords such as 'travel', 'picnic', 'children', 'festival', 'experience', 'Garden of Morning Calm', 'program', 'recreation forest', 'healing', and 'museum'. As a result of keyword analysis, we found that keywords such as 'healing', 'tree', 'experience', 'garden', and 'Garden of Morning Calm' received high public interest. We conducted word cloud analysis by extracting keywords with high frequency in total 6,245,278 titles on social media. The results showed that arboretums and botanical gardens were perceived as spaces for relaxation and leisure such as 'travel', 'picnic' and 'recreation', and that people had high interest in educational aspects with keywords such as 'experience' and 'field trip'. The demand for rest and leisure space, education, and things to see and enjoy in arboretums and botanical gardens increased than in the past. Therefore, there must be differentiation and specialization strategies such as plant collection strategies, exhibition planning and programs in establishing future operation strategies.

• Journal of People, Plants, and Environment
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• v.23 no.5
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• pp.495-506
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• 2020

Background and objective: This study conducted a conjoint analysis on early childhood teachers to find out their preferences in horticultural activities that are used as a means of education in early childhood education. Methods: For the conjoint analysis, five attributes of horticultural activities were selected. Attribute 1 was method of horticultural activities, divided into two levels: exclusive and integrated horticultural activities. Attribute 2 was object of horticultural activities, divided into three levels: fruit crops, vegetable crops, and floricultural crops. Attribute 3 was activity type, divided into three levels: big and small group, free choice, and outdoor play. Attribute 4 was place for horticultural activities, and divided into two levels: indoors and outdoors. Attribute 5 was time for horticultural activities, divided into two levels: 30 minutes and 30-60 minutes. The orthogonal design was used to extract 20 profiles, after which we conducted a survey on 320 early childhood teachers and analyzed the valid responses from 257 teachers. Results: The preference of early childhood teachers showed highest importance in object (29.1%), followed by activity type (23.2%), activity method (17.4%), time (16.1%), and place (14.2%) (Pearson's R = .591, p = .008). By level of each attribute, the importance was high in exclusive horticultural activities for activity method, big and small group for activity type, vegetable and floricultural crops for object, indoors for place, and 30 minutes for time. Conclusion: The horticultural program preferred by early childhood teachers is comprised of big and small group activities using vegetable and floricultural crops, carried out indoors for 30 minutes as an exclusive class.

• Journal of People, Plants, and Environment
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• v.23 no.1
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• pp.115-123
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• 2020

With the aim of accumulating data that can be used to infer the basis for the acculturation of trees called 'Goe,' this study aims to identify the survival status of the pagoda and zelkova trees known as 'Goe' trees by comparing and analyzing the status of pagoda trees (Sophora japonica) and zelkova tree (Zelkova serrata) designated as a protected tree in Korea. The results of this study are as follows; Zelkova serrata designated as a protected tree grows the most, with 2,147 trees (29.4%) in Cheonnam, followed by Chungnam (16.5%) and Gyeongbuk (14.4%). However, Sophora japonica showed a different result from zelkova Serrata as the total number of 210 Sophora japonica (55.7%) in Gyeongbuk and Daegu is much larger than that of zelkova Serrata. As a result, in the Yeongnam region, where the Confucianism of Yeongnam was actively practiced, the existence of Sophora japonica is much larger than that of the Zelkova Serrata, which is not a coincidence, and it is difficult to determine it only based on their flora and planting distribution. Results of comparing protected trees of Sophora japonica and Zelkova Serrata showed that the average age of Zelkova Serrata wass 289 years, while that of Sophora japonica was 302 years, and that the average height of Zelkova Serrata wass 18 m, which is higher than the height of 16 m of Sophora japonica. The average diameter at breast height of Zelkova Serrata was 398 cm and that of Sophora japonica was 314 cm, which indicates that Zelkova Serrata is relatively big. Therefore, it can be assumed that Zelkova Serrata has a larger growth potential than Sophora japonica, and the possibility of growth as a big tree is also high, but it seems that the explanation that "they are relatively long-lived" is not clearly determined.

• Journal of the Korean Society for information Management
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• v.40 no.4
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• pp.329-349
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• 2023

With the rapid development of information and communication technology, the speed of data production has increased rapidly, and this is represented by the concept of big data. Discussions on quality and reliability are also underway for big data whose data scale has rapidly increased in a short period of time. On the other hand, small data is minimal data of excellent quality and means data necessary for a specific problem situation. In the field of culture and arts, data of various types and topics exist, and research using big data technology is being conducted. However, research on whether basic information about culture and arts institutions is accurately provided and utilized is insufficient. The basic information of an institution can be an essential basis used in most big data analysis and becomes a starting point for identifying an institution. This study collected data dealing with the basic information of culture and arts institutions to define common metadata and constructed small data in the form of a knowledge graph linking institutions around common metadata. This can be a way to explore the types and characteristics of culture and arts institutions in an integrated way.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.15-31
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• 2023

In this study, we propose a novel approach to analyze big data related to patents in the field of smart factories, utilizing the Latent Dirichlet Allocation (LDA) topic modeling method and the generative artificial intelligence technology, ChatGPT. Our method includes extracting valuable insights from a large data-set of associated patents using LDA to identify latent topics and their corresponding patent documents. Additionally, we validate the suitability of the topics generated using generative AI technology and review the results with domain experts. We also employ the powerful big data analysis tool, KNIME, to preprocess and visualize the patent data, facilitating a better understanding of the global patent landscape and enabling a comparative analysis with the domestic patent environment. In order to explore quantitative and qualitative comparative advantages at this juncture, we have selected six indicators for conducting a quantitative analysis. Consequently, our approach allows us to explore the distinctive characteristics and investment directions of individual countries in the context of research and development and commercialization, based on a global-scale patent analysis in the field of smart factories. We anticipate that our findings, based on the analysis of global patent data in the field of smart factories, will serve as vital guidance for determining individual countries' directions in research and development investment. Furthermore, we propose a novel utilization of GhatGPT as a tool for validating the suitability of selected topics for policy makers who must choose topics across various scientific and technological domains.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1105-1115
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• 2015

Big data analysis for disaster have been recently started especially to text data such as social media. Social data usually supports for the final two stages of disaster management, which consists of four stages: prevention, preparation, response and recovery. Otherwise, big data analysis for meteorologic data can contribute to the prevention and preparation. This motivated us to review big data technologies dealing with non-text data rather than text in natural disaster area. To this end, we first explain the main keywords, big data, data mining and machine learning in sec. 2. Then we introduce the state-of-the-art machine learning techniques in meteorology-related field sec. 3. We show how the traditional machine learning techniques have been adapted for climatic data by taking into account the domain specificity. The application of these techniques in natural disaster response are then introduced (sec. 4), and we finally conclude with several future research directions.