• Journal of Multimedia Information System
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• v.8 no.4
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• pp.301-308
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• 2021

The purpose of this study is to analyze the oral records of sports development contributors to explore the direction of big data research on sports development contributors in the future. To this end, the audio file produced in the interview with Lee00, a sports development contributor, was converted into text. The major themes were extracted by analyzing these oral records. The sub-themes were extracted in chronological order. Keywords were extracted by analyzing sub-themes. And the extracted keywords are searched in Google search engine to find related topics and to use them. A Google search for the topic 'Mt. Inwang' extracted from the oral archives of Lee00, a contributor to the development of sports, finds newspaper articles about President Moon Jae-in's climbing Mt. Inwang and opening up Mt. Bukhan. In addition, articles about Mt. Inwang and mountain climbers that the narrator In-jeong Lee speaks are searched for. Through these articles, you can Deriving the theme of the museum exhibition, Collection of museum exhibits, Use as climbing education material.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.361-368
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• 2022

This study seeks to determine the research trend of late adolescents by utilizing big data. Also, seek for research trends related to activity participation, treatment, and mediation to provide academic implications. For this process, gathered 1.000 academic papers and used TF-IDF analysis method, and the topic modeling based on co-occurrence word network analysis method LDA (Latent Dirichlet Allocation) to analyze. In conclusion this study conducted analysis of activity participation, treatment, and mediation of late adolescents by TF-IDF analysis method, co-occurrence word network analysis method, and topic modeling analysis based on LDA(Latent Dirichlet Allocation). The results were proposed through visualization, and carries significance as this study analyzed activity, treatment, mediation factors of late adolescents, and provides new analysis methods to figure out the basic materials of activity participation trends, treatment, and mediation of late adolescents.

• 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.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.279-285
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• 2023

This study provides academic implications by considering trends of domestic research regarding therapy for Mental disorder schizophrenia and psychosocial. For the analysis of this study, text mining with the use of R program and social network analysis method have been used and 65 papers have been collected The result of this study is as follows. First, collected data were visualized through analysis of keywords by using word cloud method. Second, keywords such as intervention, schizophrenia, research, patients, program, effect, society, mind, ability, function were recorded with highest frequency resulted from keyword frequency analysis. Third, LDA (latent Dirichlet allocation) topic modeling result showed that classified into 3 keywords: patient, subjects, intervention of psychosocial, efficacy of interventions. Fourth, the social network analysis results derived connectivity, closeness centrality, betweennes centrality. In conclusion, this study presents significant results as it provided basic rehabilitation data for schizophrenia and psychosocial therapy through new research methods by analyzing with big data method by proposing the results through visualization from seeking research trends of schizophrenia and psychosocial therapy through text mining and social network analysis.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.449-455
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• 2023

The purpose of this study is to explore adolescent depression, increase understanding of social problems, and develop prevention and intervention strategies. As a research method, social big data was used to collect information related to 'youth depression', and related factors were identified through data mining and analysis of related rules. We used 'Sometrend Biz Tool' to collect and clean data from the web and then analyzed data in various languages. The study found that online articles about depression decreased during the school holidays (January to March), then increased from March to the end of June, and then decreased again from July. Therefore, it is important to establish a government-wide depression management monitoring system that can detect risk signs of adolescent depression in real time. In addition, regular stress relief and mental health education are needed during the semester, and measures must be prepared to deal with at-risk youth who share their depressed feelings in cyberspace. Results from these studies can be expected to provide important information in investigating and preventing youth depression and to contribute to policy development and intervention.

• Journal of KIBIM
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• v.13 no.4
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• pp.106-114
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• 2023

In this study, we developed a method to quantify urban social costs using mobile sensing data, providing a novel approach to urban planning. By collecting and analyzing extensive mobile data over time, we transformed travel patterns into measurable social costs. Our findings highlight the effectiveness of big data in urban planning, revealing key correlations between transportation modes and their associated social costs. This research not only advances the use of mobile data in urban planning but also suggests new directions for future studies to enhance data collection and analysis methods.

• Asia pacific journal of information systems
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• v.28 no.4
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• pp.308-319
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• 2018

This paper aims at providing valuable insights on Financial Fraud Detection on a mobile money transactional activity. We have predicted and classified the transaction as normal or fraud with a small sample and massive data set using Azure and Spark ML, which are traditional systems and Big Data respectively. Experimenting with sample dataset in Azure, we found that the Decision Forest model is the most accurate to proceed in terms of the recall value. For the massive data set using Spark ML, it is found that the Random Forest classifier algorithm of the classification model proves to be the best algorithm. It is presented that the Spark cluster gets much faster to build and evaluate models as adding more servers to the cluster with the same accuracy, which proves that the large scale data set can be predictable using Big Data platform. Finally, we reached a recall score with 0.73, which implies a satisfying prediction quality in predicting fraudulent transactions.

• Journal of ILASS-Korea
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• v.20 no.4
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• pp.223-229
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• 2015

This paper was designed to investigate emission characteristics of regulated pollutants (CO, HC, NOx and PM) from 134 diesel and gasoline passenger cars based on emission standards according to fuel additives. The experiments using chassis dynamometer were conducted under NEDC and CVS-75 modes. Comparison for fuel additive management and test between Korea, USA, EU and Japan, Korea was more strict than others. The fuel additives of this study was satisfied within fuel manufacturing standards. For with/without fuel additives according to diesel emission standards, NOx of EURO 4 and EURO 5 showed a relatively similar tendency. In the case of PM reduction rate, EURO 5 was over 20% increased than EURO 4. In the case of standard deviation/average ratio for gasoline vehicles, variation interval was big for LEV 23.3~58% and ULEV 31.6~56.4%. Following the imposition of stricter regulations (EURO 5 and ULEV), difference rate for standard deviation was big. Especially, in the case of diesel vehicles, difference rate for NOx 68% and PM 48% was most big. The results of present study will be of assistance in completing the legislative process and will provide basic data to set up emission standards for fuel additives in Korea.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.251-259
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• 2022

This study investigates the impact of ownership structures and audit quality on carbon emission disclosure. It also examines how audit quality affects the relationship between ownership structures and carbon emission disclosure. This research includes 106 standalone sustainability reports from non-financial companies that were listed on the Indonesia Stock Exchange (IDX) between 2015 and 2018. Our findings show that family and concentrated ownerships convey less information about carbon emissions. Our results fail to demonstrate that disclosure of carbon emissions could be a corporation's approach to respond to stakeholder pressure and public visibility and to provide legitimacy for its existence. We also find a positive and significant association between high-quality (Big4) auditors and carbon emission performance. Our further result suggests that Big4 auditors seem to compromise their high standard quality on auditing family and concentrated ownership firms. They fail to influence their family and concentrated ownership clients to be socially responsible. Policymakers should support the existence of Big4 auditors as a driver of carbon emission performance. Top management should be proactive to tackle carbon emission issues by adopting stakeholder-driven mechanisms and establishing legitimacy with society. Nevertheless, the involvement of family and highly concentrated shareholders in decision-making processes and information disclosure should not be encouraged.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.11
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• pp.2176-2183
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• 2017

The value of Big-Data which is a main factor of the fourth Industrial Revolution enhances industrial productivity in private sector and provides administrative services for nations and corporates in public sector. ICT-developed countries are coming up with Big-Data application in public sector rapidly. Especially, when it comes to social crisis management, they are equipped with pre-forcasting system. Korean Government also emphasizes Big-Data application in public sector for the social crisis management. But the reality where the overall infrastructure vulnerability reveals requires preparation and operation of measurement for social problems. Accordingly, we need to analyze Big-Data application problem and benchmark the precedented cases, thereby, direct policy diversity. Hence, this paper proposes the roles and rules of E-government analyzing problems from Big-Data application. The following policy proposes open Information and legal&institutional improvement, Big-Data service considerations threatening privacy issues in Big-Data ecosystem, necessity of operational and analytical technology for Big-Data and related technology in technical implication of Big-Data.