• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.381-388
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• 2023

This thesis is purposed to develop a regional-customized education program selection model using big data analysis. Based on the literature review, the concepts and characteristics of big data and lifelong education are analyzed. In addition, this thesis presents how to collect the data for lifelong education and to use big data suitable for the characteristics of lifelong education. Based on these results, a regional- customized lifelong education program selection model is developed. The regional customized lifelong education program model is developed by the following six steps. The customized education program model proposed in this study has a high degree of flexibility in terms of practical use, as it can be utilized in real-time data provision methods such as the nationally approved Lifelong Learning Personal Status Survey without the need for analysis one year later, allowing for selective analysis and future predictions. It is clear that there is a significant need and value for big data in the education field. Furthermore, all programs used in the sample model are provided free of charge, and due to the programming nature, the community is actively engaged in exchanges, making it very easy to modify and improve for the development of a more complete education program model in the future.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4189-4200
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• 2021

In this study, the relationship between the environmental radiation dose rate and meteorological variables was investigated with multiple regression analysis and big data of those variables. The environmental radiation dose rate and 36 different meteorological variables were measured on Ulleung Island, Republic of Korea, from 2011 to 2015. Not all meteorological variables were used in the regression analysis because the different meteorological variables significantly affect the environmental radiation dose rate during different periods, and the degree of influence changes with time. By applying the Pearson correlation analysis and stepwise selection methods to the big dataset, the major meteorological variables influencing the environmental radiation dose rate were identified, which were then used as the independent variables for the regression model. Subsequently, multiple regression models for the monthly datasets and dataset of the entire period were developed.

• The Journal of the Korea Contents Association
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• v.14 no.9
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• pp.538-555
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• 2014

Systematic studies have been rarely conducted on the acceptance of big data technology despite the technology drawing much attention from academia, industry and general public. With big data technology still being in the infant stage in Korea, a study model was constructed in this paper by integrating the innovation diffusion theory and the task technology fit theory with this technology acceptance model (TAM) as the central framework to make big data technology more readily acceptable in the country, and the aim of making big data technology readily acceptable was expanded as the moderator variable of the TAM. The results of this study showed that "subjective norm" and "task technology fit" showed the most significant effect as the exogenous variables of the TAM. In addition, the "innovative characteristic of the organization" was the significant exogenous variable affecting the intention to accept big data technology to those "technology utilizers" that try to come up with new services or products that are technology-based; however, "subjective norm" was the rather significant factor affecting those simple "technology users". Finally, a significant difference was seen in the verification of mediation effect.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2286-2304
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• 2022

Blockchain is a distributed ledger that combines technologies such as cryptography, consensus mechanism, peer-to-peer transmission, and time stamping. The rapid development of blockchain has attracted attention from all walks of life, but storage scalability issues have hindered the application of blockchain. In this paper, a scalable blockchain storage model based on Distributed Hash Table (DHT) and the InterPlanetary File System (IPFS) was proposed. This paper introduces the current research status of the scalable blockchain storage model, as well as the basic principles of DHT and the InterPlanetary File System. The model construction and workflow are explained in detail. At the same time, the DHT network construction mechanism, block heat identification mechanism, new node initialization mechanism, and block data read and write mechanism in the model are described in detail. Experimental results show that this model can reduce the storage burden of nodes, and at the same time, the blockchain network can accommodate more local blocks under the same block height.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2384-2392
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• 2015

The technical development and practical applications of big-data for health is one hot topic under the banner of big-data. Big-data medical image fusion is one of key problems. A new fusion approach with coding based on Spherical Coordinate Domain (SCD) in Wireless Sensor Network (WSN) for big-data medical image is proposed in this paper. In this approach, the three high-frequency coefficients in wavelet domain of medical image are pre-processed. This pre-processing strategy can reduce the redundant ratio of big-data medical image. Firstly, the high-frequency coefficients are transformed to the spherical coordinate domain to reduce the correlation in the same scale. Then, a multi-scale model product (MSMP) is used to control the shrinkage function so as to make the small wavelet coefficients and some noise removed. The high-frequency parts in spherical coordinate domain are coded by improved SPIHT algorithm. Finally, based on the multi-scale edge of medical image, it can be fused and reconstructed. Experimental results indicate the novel approach is effective and very useful for transmission of big-data medical image(especially, in the wireless environment).

• The Journal of Asian Finance, Economics and Business
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• v.8 no.12
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• pp.377-389
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• 2021

This study aims at investigating the effects of big accounting data on the sustainable business growth of listed firms in Thailand. In addition, it examines the mediating effects of accounting information quality and decision-making effectiveness and the moderating effects of digital innovation on the research relationships. The study's useful samples are the 289 listed Thai companies. To examine the research relationships, the structural equation model and multiple regression analysis are used in this study. According to the results of this study, big accounting data has a significant effect on accounting information quality, decision-making effectiveness, and sustainable business growth. Next, accounting information quality significantly affects decision-making effectiveness and sustainable business growth. Similarly, decision-making effectiveness significantly affects sustainable business growth. Both accounting information quality and decision-making effectiveness mediate the big accounting data-sustainable business growth relationships. Lastly, digital innovation moderates the effects of accounting information quality and decision-making effectiveness on sustainable business growth. Accordingly, In conclusion, big accounting data has emerged as a key source of sustainable competitive advantage. As a result, to succeed in competitive environments, businesses must have a thorough understanding of big accounting data.

• Asia pacific journal of information systems
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• v.31 no.2
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• pp.185-196
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• 2021

The paper aims to analyze and predict sales of liquor in the state of Iowa by applying machine learning algorithms to models built for prediction. We have taken recourse of Azure ML and Spark ML for our predictive analysis, which is legacy machine learning (ML) systems and Big Data ML, respectively. We have worked on the Iowa liquor sales dataset comprising of records from 2012 to 2019 in 24 columns and approximately 1.8 million rows. We have concluded by comparing the models with different algorithms applied and their accuracy in predicting the sales using both Azure ML and Spark ML. We find that the Linear Regression model has the highest precision and Decision Forest Regression has the fastest computing time with the sample data set using the legacy Azure ML systems. Decision Tree Regression model in Spark ML has the highest accuracy with the quickest computing time for the entire data set using the Big Data Spark systems.

• 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 Information Processing Systems
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• v.16 no.2
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• pp.377-393
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• 2020

Freight management systems require a new business model for rapid decision making to improve their business processes by dynamically analyzing the previous experience data. Moreover, the amount of data generated by daily business activities to be analyzed for making better decisions is enormous. Online-to-offline or offline-to-online (O2O) is an electronic commerce (e-commerce) model used to combine the online and physical services. Data analysis is usually performed offline. In the present paper, to extend its benefits to online and to efficiently apply the big data analysis to the freight management system, we suggested a system architecture based on O2O services. We analyzed and extracted the useful knowledge from the real-time freight data for the period 2014-2017 aiming at further business development. The proposed system was deemed useful for truck management companies as it allowed dynamically obtaining the big data analysis results based on O2O services, which were used to optimize logistic freight, improve customer services, predict customer expectation, reduce costs and overhead by improving profit margins, and perform load balancing.

• Journal of Integrative Natural Science
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• v.14 no.4
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• pp.176-182
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• 2021

We live in the age of artificial intelligence, and big data and artificial intelligence education are no longer just for majors, but are required to be able to handle non-majors as well. Software and artificial intelligence education for non-majors is not just a general education, it creates talents who can understand and utilize them, and the quality of education is increasingly important. Through such education, we can nurture creative talents who can create and use new values by fusion with various fields of computing technology. Since 2015, many universities have been implementing software-oriented colleges and AI-oriented colleges to foster software-oriented human resources. However, it is not easy to provide AI basic statistics education of big data analysis deception to non-majors. Therefore, we would like to present a big data education model for non-majors in big data analysis so that big data analysis can be directly applied.