• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.351-354
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• 2014

The emergence of big data has brought many changes to the database management environment. the each amount of big data will increase, but each data size is smaller and simpler. This feature was required to a new data processing techniques. Accordingly, A variety database technology was provided to Specializing in big data processing. It is defined as NoSQL. NoSQL is how to use each different, according to the data characteristics. It is difficult to define one. In this paper, Classified according to the characteristics of each type of NoSQL Appropriate NoSQL is proposed.

• Journal of People, Plants, and Environment
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• v.23 no.2
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• pp.233-243
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• 2020

This study collects social big data used in various fields in the past 9 years and explains the patterns of major keywords of the arboretums and botanical gardens to use as the basic data to establish operational strategies for future arboretums and botanical gardens. A total of 6,245,278 cases of data were collected: 4,250,583 from blogs (68.1%), 1,843,677 from online cafes (29.5%), and 151,018 from knowledge search engine (2.4%). As a result of refining valid data, 1,223,162 cases were selected for analysis. We came up with keywords through big data, and used big data program Textom to derive keywords of arboretums and botanical gardens using text mining analysis. As a result, we identified keywords such as 'travel', 'picnic', 'children', 'festival', 'experience', 'Garden of Morning Calm', 'program', 'recreation forest', 'healing', and 'museum'. As a result of keyword analysis, we found that keywords such as 'healing', 'tree', 'experience', 'garden', and 'Garden of Morning Calm' received high public interest. We conducted word cloud analysis by extracting keywords with high frequency in total 6,245,278 titles on social media. The results showed that arboretums and botanical gardens were perceived as spaces for relaxation and leisure such as 'travel', 'picnic' and 'recreation', and that people had high interest in educational aspects with keywords such as 'experience' and 'field trip'. The demand for rest and leisure space, education, and things to see and enjoy in arboretums and botanical gardens increased than in the past. Therefore, there must be differentiation and specialization strategies such as plant collection strategies, exhibition planning and programs in establishing future operation strategies.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.123-131
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• 2016

Process mining in big data environment utilize a number of data were generated from the business process. It generates lots of knowledge and insights regarding implementation and improvement of the process through the event log of the company's enterprise resource planning (ERP) system. In recent years, various research activities engaged with the audit work of company organizations are trying actively by using the maximum strength of the mining process. However, domestic studies on applicable sales auditing system for the process mining are insufficient under big data environment. Therefore, we propose process-mining methods that can be optimally applied to online and traditional auditing system. In advance, we propose continuous monitoring information system that can early detect and prevent the risk under the big data environment by monitoring risk factors in the organizations of enterprise. The scope of the research of this paper is to design a pre-verification system for risk factor via practical examples in sales auditing. Furthermore, realizations of preventive audit, continuous monitoring for high risk, reduction of fraud, and timely action for violation of rules are enhanced by proposed sales auditing system. According to the simulation results, avoidance of financial risks, reduction of audit period, and improvement of audit quality are represented.

• Korean Journal of Construction Engineering and Management
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• v.19 no.3
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• pp.89-96
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• 2018

This study derived big issues in overseas construction through big data analysis. To derive big issues in overseas construction, candidate groups of big issues were identified through big data analysis targeting 53,759 issues including 39,436 issues from major portal sites, 10,387 issues from daily newspapers, and 336 issues in construction magazines from Oct. 1, 2016 to Sep. 30, 2017. The main results are as follows: First, the main issues of overseas construction for the past one year showed that markets were concentrated in Middle East Asia and most of them were low-price order plant projects, which revealed the limitations. Although orders of overseas construction were slightly upward in the first half of 2017 compared to previous year, overseas construction orders are still unstable due to uncertainties in the international affairs and drops in oil prices. Second, the interest topics based on the 8th core keywords of overseas construction among the overseas construction issues for the past one year showed that region (29.9%), corporation environment (22.0%), profitability (17.0%), organizations (15.1%), projects (5.2%), market environment (3.6%), policy and system (3.6%), and education (3.5%) in the order of interest. Third, 10 core issues that have expandability and persistence of discourse were extracted out of 30 issue candidates with regard to eight keywords. Based on the extracted issues, detailed analysis on each of the core issues in overseas construction and correlation analysis between 10 core issues were conducted.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.93-96
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• 2018

Big datatics 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. R, a tool that can analyze big data, is a language and environment that enables information analysis of statistical bases. In this thesis, we use this to analyze the Bible data. R is used to investigate the frequency of what text is distributed and analyze the Bible through analysis of social network.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.4
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• pp.721-728
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• 2013

Big data is one of the most spotlighted technological trends in these days, enabling new methods to handle huge volume of complicated data for a broad range of applications. Real-time network traffic analysis essentially deals with big data, which is comprised of different types of log data from various sensors. To tackle this problem, in this paper, we devise a big data based platform, RENTAP, to detect and analyse malicious network traffic. Focused on military network environment such as closed network for C4I systems, leading big data based solutions are evaluated to verify which combination of the solutions is the best design for network traffic analysis platform. Based on the selected solutions, we provide detailed functional design of the suggested platform.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.943-961
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• 2020

With the advent of IoT technology and Big Data computing, the importance of WSNs (Wireless Sensor Networks) has been on the rise. For energy-efficient and collection-efficient delivery of any sensed data, lots of novel wireless medium access control (MAC) protocols have been proposed and these MAC schemes are the basis of many IoT systems that leads the upcoming fourth industrial revolution. WSNs play a very important role in collecting Big Data from various IoT sensors. Also, due to the limited amount of battery driving the sensors, energy-saving MAC technologies have been recently studied. In addition, as new IoT technologies for Big Data computing emerge to meet different needs, both sensors and sinks need to be mobile. To guarantee stability of WSNs with dynamic topologies as well as frequent physical changes, the existing MAC schemes must be tuned for better adapting to the new WSN environment which includes energy-efficiency and collection-efficiency of sensors, coverage of WSNs and data collecting methods of sinks. To address these issues, in this paper, a self-organization scheme for mobile sensor networks with mobile multiple sinks has been proposed and verified to adapt both mobile sensors and multiple sinks to 3-dimensional group management MAC protocol. Performance evaluations show that the proposed scheme outperforms the previous schemes in terms of the various usage cases. Therefore, the proposed self-organization scheme might be adaptable for various computing and networking environments with big data.

• The Journal of KAIRB
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• v.4 no.1
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• pp.16-22
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• 2022

Purpose: The traditional ethical study only suggests a blurred insight on the research using medical big data, especially in this rapid-changing and demanding environment which is called "4th Industry Revolution." Current institutional/ethical issues in big data research need to approach with the thoughtful insight of past ethical study reflecting the understanding of present conditions of this study. This study aims to examine the ethical issues that are emerging in recent health care big data research. So, this study aims to survey the public perceptions on of health care big data as part of the process of public discourse and the acceptance of the utility and provision of big data research as a subject of health care information. In addition, the emerging ethical challenges and how to comply with ethical principles in accordance with principles of the Belmont report will be discussed. Methods: Survey was conducted from June 3th August to 6th September 2020. The online survey was conducted through voluntary participation through Internet users. A total of 319 people who completed the survey (±5.49%P [95% confidence level] were analyzed. Results: In the area of the public's perspective, the survey showed that the medical information is useful for new medical development, but it is also necessary to obtain consents from subjects in order to use that medical information for various research purposes. In addition, many people were more concerned about the possibility of re-identifying personal information in medical big data. Therefore, they mentioned the necessity of transparency and privacy protection in the use of medical information. Conclusion: Big data on medical care is a core resource for the development of medicine directly related to human life, and it is necessary to open up medical data in order to realize the public good. But the ethical principles should not be overlooked. The right to self-determination must be guaranteed by means of clear, diverse consent or withdrawal of subjects, and processed in a lawful, fair and transparent manner in the processing of personal information. In addition, scientific and ethical validity of medical big data research is indispensable. Such ethical healthcare data is the only key that will lead to innovation in the future.

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.117-124
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• 2024

ESG is currently a global topic, meaning environmental, social, and governance, which are three important measures of socially responsible management. It is also having a great influence on improving competitiveness in the global market and enhancing corporate image. In this study, ESG in Korea was analyzed through big data, and four central keywords of ESG management in China based on Chinese data were derived. These four keywords are environment, management, corporate event, and quality certification. In addition, we want to understand the ESG perspective of China by studying ESG cases in China. Through this, we will be able to compare and analyze the differences between ESG approaches and key points between Korea and China.

• Journal of Convergence for Information Technology
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• v.7 no.1
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• pp.55-60
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• 2017

In order to provide intelligent services without human intervention in the Internet of Things environment, it is necessary to analyze the big data generated by the IoT device and learn the normal pattern, and to predict the abnormal symptoms such as faulty or malfunction based on the learned normal pattern. The purpose of this study is to implement a machine learning model that can predict product failure by analyzing big data generated in various devices of product process. The machine learning model uses the big data analysis tool R because it needs to analyze based on existing data with a large volume. The data collected in the product process include the information about product faulty, so supervised learning model is used. As a result of the study, I classify the variables and variable conditions affecting the product failure, and proposed a prediction model for the product failure based on the decision tree. In addition, the predictive power of the model was significantly higher in the conformity and performance evaluation analysis of the model using the ROC curve.