• Journal of the Korean Society for Marine Environment & Energy
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• v.20 no.2
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• pp.117-125
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• 2017

SNS Big-data Analysis means to find potential value from big data which has produced by the social media. In this paper, SNS Big-data has been analysed to find Korean concerns by using 24 key words from the marine and fisheries sector. Among 24 key words, seafood, shipping and Dokdo Island are the most mentioned ones. Some key words such as ocean policies and marine security that have less concerns have bess mentioned less. Also, key words that are led by government are mostly mentioned by news media, but key words that are led by private sector and have intimate relationship with people's lives are mostly mentioned by Blogs and Twitters. Therefore, reflecting close national concerns by SNS Big-data Analysis and especially resolving negative factors are the most significant part of the policy establishment. Also, differentiated promotion methods need to be prepared because the frequency of key words mentioned from each type of media are different.

• The Journal of Bigdata
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• v.2 no.2
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• pp.1-7
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• 2017

Intelligent transportation system (ITS) has been introduced to maximize the efficiency of operation and utilization of the urban traffic facilities and promote the safety and convenience of the users. With the expansion of ITS, various traffic big data such as road traffic situation, traffic volume, public transportation operation status, management situation, and public traffic use status have been increased exponentially. In this paper, we derive structural characteristics of urban traffic according to the vehicle flow by using big data network analysis. DSRC (Dedicated Short Range Communications) data is used to construct the traffic network. The results can help to understand the complex urban traffic characteristics more easily and provide basic research data for urban transportation plan such as road congestion resolution plan, road expansion plan, and bus line/interval plan in a city.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.321-324
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• 2023

본 연구는 광역시와 광역도 간의 개인적 요인과 건강수준 정도가 우울경험 여부에 영향을 미치는 변수의 중요도를 파악하고자 시도되었다. 본 연구의 자료는 질병관리청의 2021년 지역사회건강조사 데이터를 활용하였다. 광역시의 데이터는 4,602건을 이용하였고, 광역도는 19,545건의 데이터를 이용하였다. 자료 분석에 활용된 빅데이터는 R 4.3.0 for Windows를 활용하여 단어 빈도 분석과 machine learning기법인 Random Forest분석을 실시하였다. 연구결과, train 데이터와 test 데이터의 과적합(overfitting)의 문제는 발생하지 않았으며, machine learning 기법의 분류모델은 약 94% 수준으로 나타났다. 분석 결과 광역시와 광역도 간의 우울경험여부에 미치는 중요도가 각각 다르게 나타났다. 두 지역의 시민에게 미치는 우울경험의 원인을 다르게 접근함으로써 보다 더 효율적인 정책수립이 가능 할 것으로 판단된다.

• The Journal of Bigdata
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• v.5 no.2
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• pp.135-143
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• 2020

This paper proposes a pre-processing method and a dimensional reduction method in the analysis of shopping carts where there are many correlations between variables when dividing the types of consumers in the agri-food consumer panel data. Cluster analysis is a widely used method for dividing observational objects into several clusters in multivariate data. However, cluster analysis through dimensional reduction may be more effective when several variables are related. In this paper, the food consumption data surveyed of 1,987 households was clustered using the K-means method, and 17 variables were re-selected to divide it into the clusters. Principal component analysis and factor analysis were compared as the solution for multicollinearity problems and as the way to reduce dimensions for clustering. In this study, both principal component analysis and factor analysis reduced the dataset into two dimensions. Although the principal component analysis divided the dataset into three clusters, it did not seem that the difference among the characteristics of the cluster appeared well. However, the characteristics of the clusters in the consumption pattern were well distinguished under the factor analysis method.

• The Journal of Society for e-Business Studies
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• v.18 no.3
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• pp.17-43
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• 2013

This research is to assess the relationship Big Data attributes and disaster response process. The hypothesis are designed to form decision making between situation awareness and disaster response by defining major attribute of Big Data(Volume, Variety, Velocity, Complexity). It is proved whether there is a moderating effect in cause-and-effect relationship by visualizing Big Data. To test the hypotheses, it was conducted a questionnaire survey of civil servants in charge of disaster-related government employees, and collected 320 data(without 12 undependable responses). The research findings are suggested the attributes of accumulation, expandability, flexibility, real-time, analytical, combination of Big Data have a strong effect on disaster manager's situation awareness.

• Journal of the Korean Society for information Management
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• v.33 no.3
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• pp.133-154
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• 2016

Interdisciplinary approach has been recognized as one of key strategies to address various and complex research problems in modern science. The purpose of this study is to investigate the interdisciplinary characteristics and structure of the field of big data. Among the 1,083 journals related to the field of big data, multiple Subject Categories (SC) from the Web of Science were assigned to 420 journals (38.8%) and 239 journals (22.1%) were assigned with the SCs from different fields. These results show that the field of big data indicates the characteristics of interdisciplinarity. In addition, through bibliographic coupling network analysis of top 56 journals, 10 clusters in the network were recognized. Among the 10 clusters, 7 clusters were from computer science field focusing on technical aspects such as storing, processing and analyzing the data. The results of cluster analysis also identified multiple research works of analyzing and utilizing big data in various fields such as science & technology, engineering, communication, law, geography, bio-engineering and etc. Finally, with measuring three types of centrality (betweenness centrality, nearest centrality, triangle betweenness centrality) of journals, computer science journals appeared to have strong impact and subjective relations to other fields in the network.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.579-586
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• 2021

This study examines the application of big data and tasks of tourism based on the case of Jeju Special Self-Governing Province, which used big data for regional tourism policy. Through the use of big data, it is possible to understand rapidly changing tourism trends and trends in the tourism industry in a timely and detailed manner. and also could be used to elaborate existing tourism statistics. In addition, beyond the level of big data analysis to understand tourism phenomena, its scope has expanded to provide a platform for providing real-time customized services. This was made possible by the cooperative governance of industry, government, and academia for data building, analysis, infrastructure, and utilization. As a task, the limitation of budget dependence and institutional problems such as the infrastructure for building personal-level data for personalized services, which are the ultimate goal of smart tourism, and the Personal Information Protection Act remain. In addition, expertise and technical limitations for data analysis and data linkage remain.

• 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.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.693-697
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• 2023

As of 2022, dessert cafe trends are changing faster, customers' needs are becoming more demanding, and Koreans' consumption tendencies are changing rapidly, so this study investigates servicescape and customer engagement factors for dessert cafes through big data to identify servicescape and customer engagement factors.

• The Mathematical Education
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• v.62 no.2
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• pp.289-302
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• 2023

In today's digital information society, student knowledge and skills to analyze big data and make informed decisions have become an important goal of school mathematics. Integrating big data statistical projects with digital technologies in high school <Artificial Intelligence> mathematics courses has the potential to provide students with a learning experience of high impact that can develop these essential skills. This paper proposes a set of guidelines for designing effective big data statistical project-based tasks and evaluates the tasks in the artificial intelligence mathematics textbook against these criteria. The proposed guidelines recommend that projects should: (1) align knowledge and skills with the national school mathematics curriculum; (2) use preprocessed massive datasets; (3) employ data scientists' problem-solving methods; (4) encourage decision-making; (5) leverage technological tools; and (6) promote collaborative learning. The findings indicate that few textbooks fully align with these guidelines, with most failing to incorporate elements corresponding to Guideline 2 in their project tasks. In addition, most tasks in the textbooks overlook or omit data preprocessing, either by using smaller datasets or by using big data without any form of preprocessing. This can potentially result in misconceptions among students regarding the nature of big data. Furthermore, this paper discusses the relevant mathematical knowledge and skills necessary for artificial intelligence, as well as the potential benefits and pedagogical considerations associated with integrating technology into big data tasks. This research sheds light on teaching mathematical concepts with machine learning algorithms and the effective use of technology tools in big data education.