• Journal of the Korean Society of Food Culture
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• v.38 no.2
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• pp.73-82
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• 2023

Since delivery food has become a new dietary culture, this study examines consumer awareness through big data analysis. We present the direction of delivery food for healthy eating culture and identify the current state of consumer awareness. Resources for big data analysis were mainly articles written by consumers on various websites; the collection period was divided into before and after COVID-19. Results of the big data analysis revealed that before COVID-19, delivery food was recognized as a limited product as a meal concept, but after COVID-19, it was recognized as a new shopping list and a new product for home parties. This study concludes by suggesting a new direction for healthy eating culture.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.326-331
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• 2023

Korea's cultural content industry can develop into another unique tourism industry. However, since other prior studies focus on the Japanese content industry, this study identifies modern industrial trends by combining the unique characteristics of Korean content, that is, cultural content tourism, and the analysis ability of big data. The current status and direction of the cultural content tourism industry were studied by utilizing the extensive information collection and in-depth analysis capabilities of big data, and as a result, it was confirmed that the trend of the cultural content industry is related to the business aspect of cultural content, not the pure content interest of cultural content. This shows that Korean cultural contents have a strong business aspect. As a limitation, when research design was conducted using social media big data, the age, gender, etc. of the subject analyzed with unique anonymity could not be known. The Korean cultural content industry is expected to be successful in terms of business.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.125-134
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• 2022

This study aims to critically discuss the problems that need to be solved from a humanities perspective in order to utilize big data. It identifies and discusses three research problems that may arise from collecting, processing, and using big data. First, it looks at the fake information circulating with regard to problems with the data itself, specifically looking at article-type advertisements and fake news related to politics. Second, discrimination by the algorithm was cited as a problem with big data processing and its results. This discrimination was seen while searching for engineers on the portal site. Finally, problems related to the invasion of personal related information were seen in three categories: the right to privacy, the right to self-determination of information, and the right to be forgotten. This study is meaningful in that it points out the problems facing in the aspect of big data utilization from the humanities perspective in the era of big data and discusses possible problems in the collection, processing, and use of big data, respectively.

• Culinary science and hospitality research
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• v.24 no.2
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• pp.34-43
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• 2018

The objective of this study was to acquire a general and text-based awareness and recognition of cruise food hygiene through big data analytics. For the purpose, this study collected data with conducting the keyword "food hygiene, cruise" on the web pages and news on Google, during October 1st, 2015 to October 1st, 2017 (two years). The data collection was processed by SCTM which is a data collecting and processing program and eventually, 899 kb, approximately 20,000 words were collected. For the data analysis, UCINET 6.0 packaged with visualization tool-Netdraw was utilized. As a result of the data analysis, the words such as jobs, news, showed the high frequency while the results of centrality (Freeman's degree centrality and Eigenvector centrality) and proximity indicated the distinct rank with the frequency. Meanwhile, as for the result of CONCOR analysis, 4 segmentations were created as "food hygiene group", "person group", "location related group" and "brand group". The diagnosis of this study for the food hygiene in cruise industry through big data is expected to provide instrumental implications both for academia research and empirical application.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.159-165
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• 2024

This study investigates the comprehensive system for collecting intelligent learning activity data tailored to learner-centered personalized education. We compared and analyzed the characteristics of xAPI, Caliper analytics, and cmi5, which are learning activity data collection standards, and established a system that allows not only standardized data but also non-standardized learning activity data to be stored as big data for artificial intelligence learning analysis. As a result, the system was structured into five stages: defining data types, standardizing learning data using xAPI, storing big data, conducting learning analysis (statistical and AI-based), and providing learner-tailored services. The aim was to establish a foundation for analyzing learning data using artificial intelligence technology. In future research, we will divide the entire system into three stages, implement and execute it, and correct and supplement any shortcomings in the design.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.255-256
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• 2021

The performance measurement of construction projects has traditionally been evaluated as a prerequisite for successful project completion. Considering this importance, the UK and the US are operating quantitative performance measurement systems for construction projects. However, in the case of Korea, there is a limit to the use of existing methods due to the limitation of data collection. Recently, in consideration of the domestic situation, research is being conducted to measure the quantitative performance of a project by using big data including progress and project attribute information. Therefore, this study aims to present the design direction of a performance monitoring system using Quality Function Deployment.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.45-51
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• 2021

In accordance with the rapid non-face-to-face environment and mobile first strategy, the explosive increase and creation of many structured/unstructured data every year demands new decision making and services using big data in all fields. However, there have been few reference cases of using the Hadoop Ecosystem, which uses the rapidly increasing big data every year to collect and load big data into a standard platform that can be applied in a practical environment, and then store and process well-established big data in a relational database. Therefore, in this study, after collecting unstructured data searched by keywords from social network services based on Hadoop 2.0 through three virtual machine servers in the Spring Framework environment, the collected unstructured data is loaded into Hadoop Distributed File System and HBase based on the loaded unstructured data, it was designed and implemented to store standardized big data in a relational database using a morpheme analyzer. In the future, research on clustering and classification and analysis using machine learning using Hive or Mahout for deep data analysis should be continued.

• Journal of Navigation and Port Research
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• v.46 no.5
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• pp.409-417
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• 2022

The International Association of Lighthouse Authorities (IALA) utilizes a questionnaire to investigate the status of Aids to Navigation (AtoN) around the world. However, results of the IALA questionnaire have limited use because respondent understanding is inconsistent. In addition, there is uncertainty regarding the appropriateness of the questionnaire content. Furthermore, the overall response rate is low. Therefore, the status of AtoN is not clearly understood. AtoN data from around the world are generated hourly. Thus, big data solutions are required to effectively exploit the information. Digitization of analog data is an important component of building big data. Hence, the IALA has developed a Maritime Resource Name (MRN) scheme and an information exchange standard. Here, we used the AtoN information exchange standard and designed an S-201-based big data construction process that could collect and manage global AtoN information. In this study, construction of an IALA AtoN portal was proposed as the core of the construction of the AtoN big data. The process was divided into three stages. IALA AtoN portal is developed by IALA with the goal to provide various meaningful statistical analysis results based on AtoN data while managing AtoN information around the world based on S-201. If an AtoN portal capable of constructing S-201-based big data is developed, then a data collection and storage system that can gather basic S-201 AtoN data from the IALA and global AtoN management agencies could be achieved. Furthermore, insightful statistical analysis of AtoN status worldwide and changes in manufacturing technology will be possible.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.351-359
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• 2014

The log data generated by security equipment have been synthetically analyzed on the ESM(Enterprise Security Management) base so far, but due to its limitations of the capacity and processing performance, it is not suited for big data processing. Therefore the another way of technology on the big data platform is necessary. Big Data platform can achieve a large amount of data collection, storage, processing, retrieval, analysis, and visualization by using Hadoop Ecosystem. Currently ESM technology has developed in the way of SIEM (Security Information & Event Management) technology, and to implement security technology in SIEM way, Big Data platform technology is essential that can handle large log data which occurs in the current security devices. In this paper, we have a big data platform Hadoop Ecosystem technology for analyzing the security log for sure how to implement the system model is studied.

• Journal of the Korea Convergence Society
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• v.9 no.8
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• pp.25-32
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• 2018

Recently, the size and type of disasters in Korea has been diversified. However, Korea has not been able to build various information support systems to predict these disasters.Many other organizations also provide relevant information. This information is mainly provided on the Web, but most of it is not real time information. In this study, we have paid attention to support information using big data to provide better quality real - time information together with information provided by institutions. Big data has a large amount of information with real-time property, and it can make customized service using it. Among them, SNS such as Twitter and Facebook can be used as a new information collection medium in case of disaster. However, it is very difficult to retrieve necessary information from too much information, and it is difficult to collect intuitive information. For this purpose, this study develops an information support system using Twitter. The system retrieves information using the Twitter hashtag. Also, information mapping is performed on the map so that intuitive information can be grasped. For system evaluation, information extraction, degree of mapping, and recommendation speed are evaluated.