• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.373-375
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• 2016

Analyze the Big Data become a hot issue because of Smart environment, the amount of data in the world has been exploding. Result of application makes a good use of Analysis and applicate of the big data, is play an important part in application area (finance, circulation, manufacturing, disaster etc.) This paper presents an influence element for data analysis and its practical use based in result of maturity in Business process of Big Data in Electrical Commercial system.

• Journal of KIBIM
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• v.6 no.3
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• pp.24-33
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• 2016

Planning occurs from day-to-day, small-scale decisions to large-scale infrastructure investment decisions. For that reason, various attempts have been made to appropriately assist decision-making process and its optimization. Lately, initiation of a large amount of data, also known as big data has received great attention from diverse disciplines because of versatility and adoptability in its use and possibility to generate new information. Accordingly, implementation of big data and other information management systems, such as geographic information systems (GIS) and building information modeling (BIM) have received enough attention to establish each of its own profession and other associated activities. In this extent, this study illustrates a series of big data implementation cases that can provide a lesson to urban planning domain. In specific, case studies analyze how data was used to extract the most optimized solution and what aspects could be helpful in relation to planning decisions. Also, important notions about GIS and its application in various urban cases are examined.

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.59-66
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• 2021

Recently, conceptual information model is changing fast, and these changes are coming about as a result of individual tendency, social cultural, new circumstances and societal shifts within big data environment. Despite the data is growing more and more, now is the time to commit ourselves to the development of renewable, invaluable information of social/live commerce. Because we have problems with various insoluble data, we propose about deep learning prediction model-based object classification in social commerce of big data environment. Accordingly, it is an increased need of social commerce platform capable of handling high volumes of multiple items by users. Consequently, responding to rapid changes in users is a very significant by deep learning. Namely, promptly meet the needs of the times, and a widespread growth in big data environment with the goal of realizing in this paper.

• Convergence Security Journal
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• v.22 no.1
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• pp.55-60
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• 2022

This study is to analyze trends and relational structures in the energy sector related to renewable energy. For this reason, in this study, we focused on big data including SNS data. SNS utilizes the Instagram platform to collect renewable energy hash tags and use them as a word embedding method for big data analysis and social network analysis, and based on the results derived from this research, it will be used for the development of the renewable energy industry. It is expected that it can be utilized.

• Tuberculosis and Respiratory Diseases
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• v.87 no.3
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• pp.213-220
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• 2024

The continuously increasing prevalence of severe asthma has imposed an increasing burden worldwide. Despite the emergence of novel therapeutic agents, management of severe asthma remains challenging. Insights garnered from big data may be helpful in the effort to determine the complex nature of severe asthma. In the field of asthma research, a vast amount of big data from various sources, including electronic health records, national claims data, and international cohorts, is now available. However, understanding of the strengths and limitations is required for proper utilization of specific datasets. Use of big data, along with advancements in artificial intelligence techniques, could potentially facilitate the practice of precision medicine in management of severe asthma.

• The Journal of Bigdata
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• v.2 no.2
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• pp.87-93
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• 2017

The research on data quality has been performed for a long time. However, the research focused on structured data. With the recent digital revolution or the fourth industrial revolution, quality control of big data is becoming more important. In this paper, we analyze and classify big data quality types through previous research. The types of big data quality can be classified into value, data structure, process, value chain, and maturity model. Based on these comparative studies, this paper proposes a new standard, service quality of big data.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1305-1317
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• 2019

Although encryption for data protection is essential in the big data platform environment of public institutions and corporations, much performance verification studies on encryption algorithms considering actual big data workloads have not been conducted. In this paper, we analyzed the performance change of AES, ARIA, and 3DES for each of six workloads of big data by adding data and nodes in MongoDB environment. This enables us to identify the optimal block-based cryptographic algorithm for each workload in the big data platform environment, and test the performance of MongoDB by testing various workloads in data and node configurations using the NoSQL Database Benchmark (YCSB). We propose an optimized architecture that takes into account.

• Smart Media Journal
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• v.3 no.3
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• pp.26-35
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• 2014

The statistical graphics is a design field focusing on the user perception aspects for the correct information delivery and the effective understanding, with the use of the quantitative data through the information analysis, extraction, visualization process. The statistical graphics with the big data composition factor is termed as the statistical big data graphics. In the statistical graphics the visual factors are used to reduce the errors in the perception part and to successfully deliver the information. However, in the statistical big data graphics the visual factors of the enormous data are causing the cognitive discouragements. The purpose of this study is to extract the cognitive discouragement factors from the big data statistical graphics, categorizing the types of the statistical big data graphics as 'network type', 'segment type', and 'mixed type', based on their compositional shapes, and explored the characteristics according to them. Especially, based on the visual main factors in the statistical big data graphics, We extracted the cognitive discouragement factors that appear in the high visualization as the four categories: 'multi-dimensional cases', 'various color', 'information overlap', and 'legibility of the writing'.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.24 no.4
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• pp.53-73
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• 2013

This study revisits issues for Big data. Three research questions, understanding the concept of Big data, important issues of Big data research and utilization methods for library information services, are explored by the literature and practice reviews. Study results revealed several important issues of Big data including the concept in the context of real world situation, the problems with the accuracy and reliability of the data, privacy and ethical issues, and issues of intellectual property rights. With understanding these issues, a few utilization methods were introduced for Library and Information services. It was included using its analysis for developing vision, adopting Library management, supporting community services, and providing customized information services for various users. The study concluded Big data analysis would effectively provide valid evidences for all those services.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.11
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• pp.2027-2032
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• 2016

The rapid diffusion of smart phones and the development of data storage and analysis technology have made the field of big-data a promising industry in the future. In the marketing field, big-data analysis on social data can be used for understanding the needs of consumers as an effective and efficient marketing tool. Before the age of big-data, companies had relied upon the traditional methods such as questionnaire survey and marketing test in which a small number of consumers had participated. The traditional methods have still been used. Although both of big-data analysis and traditional methods are useful to understand consumers. It is need to check whether the results from both include similar implications. In this point, this study attempts to compare the results of big-data analysis with that of questionnaire survey on some cosmetics brands methods. As the results of this study, both results of big-data analysis and questionnaire survey include similar implications.