• The Journal of Bigdata
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• v.3 no.2
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• pp.11-18
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• 2018

This study is a study on domain automatic classification for domain - based quality diagnosis which is a key element of big data quality diagnosis. With the increase of the value and utilization of Big Data and the rise of the Fourth Industrial Revolution, the world is making efforts to create new value by utilizing big data in various fields converged with IT such as law, medical, and finance. However, analysis based on low-reliability data results in critical problems in both the process and the result, and it is also difficult to believe that judgments based on the analysis results. Although the need of highly reliable data has also increased, research on the quality of data and its results have been insufficient. The purpose of this study is to shorten the work time to automizing the domain classification work which was performed from manually to using machine learning in the domain - based quality diagnosis, which is a key element of diagnostic evaluation for improving data quality. Extracts information about the characteristics of the data that is stored in the database and identifies the domain, and then featurize it, and automizes the domain classification using machine learning. We will use it for big data quality diagnosis and contribute to quality improvement.

• The Journal of Bigdata
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• v.5 no.2
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• pp.43-52
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• 2020

With the development of data technology, storing and sharing of data has increased, resulting in privacy invasion. Although de-identification technology has been introduced to solve this problem, it has been proved many times that identifying individuals using de-identified data is possible. Even if it cannot be completely safe, sufficient de-identification is necessary. But current laws and regulations do not quantitatively specify the degree of how much de-identification should be performed. In this paper, we propose an appropriate de-identification criterion considering the time required for re-identification. We focused on the case of using the k-anonymity model among various privacy models. We analyzed the time taken to re-identify data according to the change in the k value. We used a re-identification method based on linkability. As a result of the analysis, we determined which k value is appropriate. If the generalized model can be developed by results of this paper, the model can be used to define the appropriate level of de-identification in various laws and regulations.

• The Journal of Bigdata
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• v.5 no.2
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• pp.85-95
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• 2020

In this study, a mobility analysis method is suggested to estimate an O/D trip demand estimation using Mobile Phone Signaling Data. Using mobile data based on mobile base station location information, a trip chain database was established for each person and daily traffic patterns were analyzed. In addition, a new algorithm was developed to determine the traffic characteristics of their mobilities. To correct the ping pong handover problem of communication data itself, the methodology was developed and the criteria for stay time was set to distinguish pass by between stay within the influence area. The big-data based method is applied to analyze the mobility pattern in inter-regional trip and intra-regional trip in both of an urban area and a rural city. When comparing it with the results with traditional methods, it seems that the new methodology has a possibility to be applied to the national survey projects in the future.

• The Journal of Bigdata
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• v.7 no.1
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• pp.193-212
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• 2022

The Bass Diffusion Model is one of the most successful models in marketing research, and management science in general. Since its publication in 1969, it has guided marketing research on diffusion. This paper illustrates the usage of the Bass diffusion model, using mobile cellular subscription diffusion as a context. We fit the bass diffusion model to three large developed markets, South Korea, Japan, and China, and the emerging markets of Vietnam, Thailand, Kazakhstan, and Mongolia. We estimate the parameters of the bass diffusion model using the nonlinear least square method. The diffusion of mobile cellular subscriptions does follow an S-curve in every case. After acquiring m, p, and q parameters we use k-Means Cluster Analysis for grouping countries into three groups. By clustering countries, we suggest that diffusion rates and patterns are similar, where countries with emerging markets can follow in the footsteps of countries with developed markets. The purpose was to predict the timing and the magnitude of the market maturity and to determine whether the data follow the typical diffusion curve of innovations from the Bass model.

• The Journal of Bigdata
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• v.7 no.2
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• pp.43-73
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• 2022

In order to improve productivity, reduce costs, and improve decision-making efficiency, which are one of the main contents of the digital transformation promotion goal, many companies are promoting the introduction of various IT for digital transformation. Information technology (IT) is a key means of determining competitiveness, and the IT adoption worldwide is increasing every year. The financial industry is also actively introducing huge amounts of IT every year to generate profits, improve work efficiency, and secure a strategic competitive advantage. Compared to some studies on the IT adoption in the public and corporate sectors, empirical studies that reflect the characteristics of the financial industry are insufficient. In this study, the purpose of this study was to derive factors affecting the IT adoption in the financial industry for the promotion of digital transformation, and to analyze weights and priorities. By revealing through data analysis that there is a difference in the relative priorities of factors in the financial IT adoption for each group, it can be used as a reference model for which factors should be considered prior to IT adoption from the perspective of each group. It will be meaningful in that it exists.

• The Journal of Bigdata
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• v.8 no.2
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• pp.55-71
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• 2023

Cloud Native refers to the Technical Maturity Level of a cloud environment that can utilize all cloud resources to fully function. In converting public sector information resources to the cloud, the characteristics of the cloud are not being used well. Therefore, in this study, the qualitative research method cloud expert interview technique and the quantitative research method used text network analysis for domestic and foreign related articles. Through this, we analyzed the utilization trends related to domestic and foreign cloud natives and the cloud policies of developed countries. Through previous research, the core components of cloud-native were examined, and the need for agile methodologies that were not addressed in previous studies was raised. It is believed that these core components will be applied in the public sector to contribute to business innovation through digital innovation. In addition, this study aims to provide important implications for the use of cloud native in Korea through an in-depth discussion on how to spread cloud native in the public sector.

• International Journal of Advanced Culture Technology
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• v.10 no.4
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• pp.94-101
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• 2022

For the purpose of this study, in order to adapt to the era of intelligent informatization in the 4th Industrial Revolution, we propose an information processing and analysis technology education plan that can solve problems through information search and collection. To this end, first, we explored the necessity and content of information processing and analysis technology in hyperconnection, hyperintelligence, and hyperconvergence under the theme of various majors in IT, focusing on understanding information technology in the software and hardware curriculum. Second, the curriculum improvement plan was proposed based on information literacy, computing thinking skills, and cooperative problem-solving skills for efficient software and hardware-linked curriculum operation based on information processing and analysis technology. Third, I would like to emphasize that it is essential to secure connectivity between other studies for future innovation in new technologies related to computer technology, machine technology, and infrastructure technology through hyperconnection, hyperintelligence, and hyperconvergence in the software and hardware curriculum. Through this, we intend to cultivate creative convergence talent required by the future society.

• Knowledge Management Research
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• v.19 no.1
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• pp.119-132
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• 2018

Many advanced products and services are emerging in the market thanks to data-based technologies such as Internet (IoT), Big Data, and AI. The construction of a system for data processing under the IoT network environment is not simple in configuration, and has a lot of restrictions due to a high cost for constructing a high performance server environment. Therefore, in this paper, we will design a development environment for large data analysis computing platform using open source with low cost and practicality. Therefore, this study intends to implement a big data processing system using Raspberry Pi, an ultra-small PC environment, and open source API. This big data processing system includes building a portable server system, building a web server for web mining, developing Python IDE classes for crawling, and developing R Libraries for NLP and visualization. Through this research, we will develop a web environment that can control real-time data collection and analysis of web media in a mobile environment and present it as a curriculum for non-IT specialists.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.193-200
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• 2019

Accruals-Quality(AQ) is an important proxy for evaluating the quality of accounting information disclosures. High-quality accounting information will provide high predictability and precision in the disclosure of earnings and will increase the response to stock prices. And high Accruals-Quality, such as mitigating heterogeneity in accounting information interpretation, provides information usefulness in capital markets. The purpose of this study is to suggest how AQ, which represents the quality of accounting information disclosure, is transformed into digitized data in real-time in combination with IT information technology and provided to financial analyst's information environment in real-time. And AQ is a framework for predictive analysis through big data log analysis system. This real-time information from AQ will help financial analysts to increase their activity and reduce information asymmetry. In addition, AQ, which is provided in real time through IT information technology, can be used as an important basis for decision-making by users of capital market information, and is expected to contribute in providing companies with incentives to voluntarily improve the quality of accounting information disclosure.

• Journal of Industrial Convergence
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• v.15 no.1
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• pp.33-42
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• 2017

The purpose of this study is to identify the current situation of bicycle rental center in Seoul through big data analysis and to find ways to improve it. For this purpose, we analyzed the open data set provided by the Seoul Metropolitan Government and the typical data which is the citizen opinion of the customer center of the Seoul City bicycle. As the result, it was found that it is better to install a bicycle rental shop in Gangdong-gu, Seoul.