• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.166-171
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• 2020

Big data processing technology that can store and analyze data and obtain new knowledge has been adjusted for importance in many fields of the society. Big data is emerging as an important problem in the field of information and communication technology, but the mind of continuous technology is rising. the R, a tool that can analyze big data, is a language and environment that enables information analysis of statistical bases. In this paper, we use this to analyze the Bible data. We analyze the four Gospels of the New Testament in the Bible. We collect the Bible data and perform filtering for analysis. The R is used to investigate the frequency of what text is distributed and analyze the Bible through social network analysis, in which words from a sentence are paired and analyzed between words for accurate data analysis.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.171-189
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• 2022

Online reviews play an essential role in the consumer's purchasing decision-making process, and thus, providing helpful and reliable reviews is essential to consumers. Previous online review helpfulness prediction studies mainly predicted review helpfulness based on the consistency of text and rating information of online reviews. However, there is a limitation in that representation capacity or review text and rating interaction. We propose a CNN-RHP model that effectively learns the interaction between review text and rating information to improve the limitations of previous studies. Multi-channel CNNs were applied to extract the semantic representation of the review text. We also converted rating into independent high-dimensional embedding vectors representing the same dimension as the text vector. The consistency between the review text and the rating information is learned based on element-wise operations between the review text and the star rating vector. To evaluate the performance of the proposed CNN-RHP model in this study, we used online reviews collected from Amazom.com. Experimental results show that the CNN-RHP model indicates excellent performance compared to several benchmark models. The results of this study can provide practical implications when providing services related to review helpfulness on online e-commerce platforms.

• Journal of Nutrition and Health
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• v.55 no.1
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• pp.1-9
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• 2022

In the past few decades, great progress has been made on understanding the interaction between nutrition and health status. But despite this wealth of knowledge, health problems related to nutrition continue to increase. This leads us to postulate that the continuing trend may result from a lack of consideration for intra-individual biological variation on dietary responses. Precision nutrition utilizes personal information such as age, gender, lifestyle, diet intake, environmental exposure, genetic variants, microbiome, and epigenetics to provide better dietary advices and interventions. Recent technological advances in the artificial intelligence, big data analytics, cloud computing, and machine learning, have made it possible to process data on a scale and in ways that were previously impossible. A big data platform is built by collecting numerous parameters such as meal features, medical metadata, lifestyle variation, genome diversity and microbiome composition. Sophisticated techniques based on machine learning algorithm can be used to integrate and interpret multiple factors and provide dietary guidance at a personalized or stratified level. The development of a suitable machine learning algorithm would make it possible to suggest a personalized diet or functional food based on analysis of intra-individual metabolic variation. This novel precision nutrition might become one of the most exciting and promising approaches of improving health conditions, especially in the context of non-communicable disease prevention.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3619-3637
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• 2022

Big data analytics offers endless opportunities for operational enhancement by extracting valuable insights from complex voluminous data. Hadoop is a comprehensive technological suite which offers solutions for the large scale storage and computing needs of Big data. The performance of Hadoop is closely tied with its configuration settings which depends on the cluster capacity and the application profile. Since Hadoop has over 190 configuration parameters, tuning them to gain optimal application performance is a daunting challenge. Our approach is to extract a subset of impactful parameters from which the performance enhancing sub-optimal configuration is then narrowed down. This paper presents a statistical model to analyze the significance of the effect of Hadoop parameters on a variety of performance metrics. Our model decomposes the total observed performance variation and ascribes them to the main parameters, their interaction effects and noise factors. The method clearly segregates impactful parameters from the rest. The configuration setting determined by our methodology has reduced the Job completion time by 22%, resource utilization in terms of memory and CPU by 15% and 12% respectively, the number of killed Maps by 50% and Disk spillage by 23%. The proposed technique can be leveraged to ease the configuration tuning task of any Hadoop cluster despite the differences in the underlying infrastructure and the application running on it.

• The Journal of Information Systems
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• v.26 no.2
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• pp.1-23
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• 2017

Purpose Facebook is a social network service that has the highest number of Monthly Active Users around the world. Hence, marketers have selected Facebook as the most important platform to get customer engagement. With respect to the customer engagement enhancement, the most popular and engaging post type in the Facebook brand fan pages related to what was usually classified as 'sweepstakes'. Sweepstakes refer to a form of gambling where the entire prize may be awarded to the winner. Which makes customers more engaged with the brand. This study aims to explore sweepstakes-oriented social media marketing approaches based on the application of big data analytics. Design/methodology/approach we collect sweepstakes data from each company based on the data crawling from the Facebook brand fan pages. The output of this study explains how companies in each category of FCB grid can design and apply sweepstakes for their social media marketing. Findings The results show that they have one thing in common across the four quadrants of FCB grid. Regardless of the quadrants, most frequently observed type is 'Simple/Quiz or Comments/Quatrains [event type of sweepstakes] + Gifticon [type of reward prize] + Image [type of message display] + No URL [Link toother website] +Single-Gift-Offer [type of reward prize payment]'. So, if the position of the brand is hard to be defined by the FCB grid model, then this general rule can be applied to all types of brands. Also some differences between the quadrants of the FCB grid were observed. This study offers several research implications by analyzing Sweepstakes-oriented social media marketing approaches in Facebook brand fan pages. By using the FCB grid model, this study provides guidance on how companies can design their sweepstakes-oriented social media marketing approaches in the context of Facebook brand fan pages by considering their context.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.79-105
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• 2023

Due to unprecedented extreme weather events such as global warming and climate change, many parts of the world suffer from severe pain, and economic losses are also snowballing. In order to address these problems, 'The Paris Agreement' was signed in 2016, and an intergovernmental consultative body was formed to keep the average temperature rise of the Earth below 1.5℃. Korea also declared 'Carbon Neutrality in 2050' to prevent climate catastrophe. In particular, it was found that the increase in temperature caused by greenhouse gas emissions hurts the environment and society as a whole, as well as the export-dependent economy of Korea. In addition, as the diversification of transportation types is accelerating, the change in means of choice is also increasing. As the development paradigm in the low-growth era changes to urban regeneration, interest in idle railway sites is rising due to reduced demand for routes, improvement of alignment, and relocation of urban railways. Meanwhile, it is possible to partially achieve the solar power generation goal of 'Renewable Energy 3020' by utilizing already developed but idle railway sites and take advantage of being free from environmental damage and resident acceptance issues surrounding the location; but the actual use and plan for these solar power facilities are still lacking. Therefore, in this study, using the big data provided by the Korea National Railway and the Renewable Energy Cloud Platform, we develop an algorithm to discover and analyze suitable idle sites where solar power generation facilities can be installed and identify potentially applicable areas considering conditions desired by users. By searching and deriving these idle but relevant sites, it is intended to devise a plan to save enormous costs for facilities or expansion in the early stages of development. This study uses various cluster analyses to develop an optimal algorithm that can derive solar power plant locations on idle railway sites and, as a result, suggests 202 'actively recommended areas.' These results would help decision-makers make rational decisions from the viewpoint of simultaneously considering the economy and the environment.

• The Journal of Information Systems
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• v.29 no.1
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• pp.225-240
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• 2020

Purpose The purpose of this study is to identify features of Gapjil and platform tyranny through South Korea's Gapjil and platform tyranny cases and to suggest countermeasures to both kinds of cases and follow-up study subjects. Methodology/approach We examined South Korea's Gapjil and platform tyranny cases by using Big Data analytics. Then we made a close examination of the two typical cases, through which we compared features and countermeasures of Gapjil and those of platform tyranny. Findings Gapjil mostly occurred at conventional companies and franchise companies, between major and minor companies, or due to lack of owner's qualifications. The features of platform tyranny were excessively monopolistic structure of platform business, inadequate legal sanctions, and features of ICT companies. Establishment of legal bases for sanctions and education for platform participants were suggested as countermeasures.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.451-453
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• 2016

대규모의 데이터가 생산되고 저장되는 정보화 시대에서 현재와 과거의 데이터를 바탕으로 미래를 추측하고 방향성을 알아갈 수 있는 빅데이터의 중요성이 강조되고 있다. 정형되지 못한 대규모 데이터를 빅데이터 분석 도구인 R을 통해 통계를 기초로 데이터의 정보분석과 정형화하도록 한다. 본 논문에서는 R을 이용하여 뉴스에서 나타나는 차세대 에너지 관련 빅데이터를 분석한다. 뉴스 기사에서 차세대 에너지 관련 데이터를 수집하고 수집된 키워드를 이용하여 근미래의 효율적인 차세대 에너지의 등장을 예측한다. 에너지 산업의 추진에 대한 흐름과 방향성을 제시하고 의사결정을 위한 기술적 과제를 도출함으로 탄력적인 경영과 의사결정에 도움을 주며 기술적 문제의 근원을 사전에 예측하고 방지할 수 있을 것으로 보여진다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.642-644
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• 2022

In this paper, we propose a dynamic Web-Dashboard application so that resource information can be accessed anytime, anywhere, and management efficiency improvement analysis through big data analysis using various progressive analysis functions. It provides visualization and analytics to provide rich analytical insights to track resource location, predict repurchase duration, or find cost savings and efficiencies.

• The Journal of Economics, Marketing and Management
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• v.8 no.2
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• pp.1-12
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• 2020

Purpose: The rise of the phrase Work Life Balance was bought up in 1986 when amid many Americans there was prevalence of detrimental work place practices like neglecting families, leisure activities and friends in order to achieve their study place goals. The significance of work-life balance has been gaining ground in recent years to grasp a wider range of groups, including students. Searching and finding a balance can be complex and challenging for many individuals and students. Research design, data and methodology: Through this paper we will explore how students balance the competing demands of work, study, and social activities. Several factors have increased imbalances within Educational organizations, and technology specifically has been influential. However, technology also provides a novel solution to this organizational performance management issue. A Study-Life Optimization model (SLO) is suggested, which incorporates information systems, analytics, and decision support into a Smart Service System. A general framework for this model, detailing data collection, measurement, and ethical issues is explained briefly. Results: Outcomes include improved WLB, greater perceived quality of life, and increased Educational organizational performance. Conclusions: This paper contributes to the relevant literature as it pays attention to the various students' of varying lifestyles school-work-personal lives. Findings of this study will provide a meaningful of the Work/school-life balance issues faced by students. The research could be helpful to the various stakeholders of a University, the curriculum designers, program coordinators etc.