• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.149-156
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• 2021

This study started from the question of what we think about big data as the term "big data" was used and discussed in our daily lives in the era of the 4th industrial revolution. For the analysis, the final 30 Q samples were selected based on prior research related to big data, and 23 respondents were secured for Q analysis, and the following results were derived. First, the explanatory power of each type was 34.30% for , 8.03% for , 7.21% for , and 6.24% for , showing a total of 55.69%. Second, the Q sample emphasized by respondents by each type shows various occupational distributions in , and for 'big data', it is 'digital' and future'. So they were named 「Digital Type」. In , the distribution of 'social workers' was high, and for 'big data', 'future', 'collaboration', 'welfare', 'local residents', and 'defense' were emphasized. It was named 「welfare type」. In , the job distribution of respondents appeared evenly, and it was named as 「Convergence Type」. Because it emphasized statements such as 'convergence', 'digital', 'future', and 'sports'. is composed of association officials, sports instructors, and graduate students, and was named 「Artificial Intelligence Type」, because it emphasizes 'artificial intelligence', 'new paradigm', 'network', and 'sports'. In the age of knowledge industrialization and knowledge informatization that followed industrialization and informatization, how to process and utilize the numerous data accumulated over the years is an important task. Right now, in sports, more than anything else, it is necessary to continuously seek ways to utilize and activate accumulated big data.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.4
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• pp.187-210
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• 2013

The consumption through e-journal consortia makes researchers locate and use academic resources and information extensively with comparatively cheap costs. This study analyzed and investigated substantive benefits of the big deal contracts for e-journal subscriptions in terms of efficient information use. In other words, this study compare concretely the differences in efficiency of using information between large-size institutions and small-size institutions who participate in the e-journal big deal contracts. This study suggests solutions for the problems which occur persistently and repeatedly in the big deal and new counter plans which can replace the current methods of big deal contracts in a long-term perspective by revealing the gaps of acquiring and using information by the size of participating institutions.

• Informatization Policy
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• v.21 no.4
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• pp.20-39
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• 2014

The age of big data has not only opened new opportunities for economic growth in various industries, but it has also created new problems related to personal information protection and privacy invasion. Given this situation, Korea's communications commission has proposed a big data guideline that specifies how companies should collect and utilize personal information in the big data environment. However, this guideline is more focused on industrial development than personal information protection, and it contains many features that conflict with personal information protection law as it currently exists. As a result, civic groups strongly oppose the guideline, as it may create serious privacy issues for subjects of information gathering. Thus, this paper analyses the limitations of the guideline by comparing it with domestic and foreign laws about personal information protection and privacy. We also discuss the direction of legalization and institutionalization with respect to the secure use of big data.

• Spatial Information Research
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• v.22 no.6
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• pp.13-21
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• 2014

This study defines concept and service framework of Geo-Spatial Big Data(GSBD). The major concept of the GSBD is formulated based on the 7V characteristics: the general characteristics of big data with 3V(Volume, Variety, Velocity); Geo-spatial oriented characteristics with 4V(Veracity, Visualization, Versatile, Value). GSBD is the technology to extract meaningful information from Geo-spatial fusion data and support decision making responding with rapidly changing activities by analysing with almost realtime solutions while efficiently collecting, storing and managing structured, semi-structured or unstructured big data. The application area of the GSBD is segmented in terms of technical aspect(store, manage, analyze and service) and public/private area. The service framework for the GSBD composed of modules to manage, contain and monitor GSBD services is suggested. Such additional studies as building specific application service models and formulating service delivery strategies for the GSBD are required based on the services framework.

• The Journal of the Convergence on Culture Technology
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• v.5 no.2
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• pp.361-366
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• 2019

The amount of data created in the present intelligence information society is beyond imagination. Big data has a great diversity from every information via SNS and internet to the one created by government and enterprises. This various data is close at hand having infinite value as same as crude oil. Big data analysis and utilization by data mining over every areas in the modern industrial society is getting more important for finding useful correlation and strengthening forecasting power against the future uncertainty. Efficient management and utilization of big data produced by complex modern society will be researched in this paper. Also it addresses strategies and methods for securing overall industrial competitiveness, synergy creation among industries, cost reduction and effective application based on big data in the $4^{th}$ industrial revolution era.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.57-63
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• 2022

The purpose of this study is to provide basic data on policy development using big data analysis and machine learning algorithms as part of preparing measures to prevent child abuse. In order to analyze big data for developing machine learning algorithms to prevent child abuse, frequency analysis, related word analysis, and emotional analysis were performed after defining academic databases and social network service data as big data. related words, and emotional analysis were conducted. As a result of the study, a preventive child abuse algorithm can be developed by preparing a data collection and sharing network system to prevent child abuse from the perspective of children affected by child abuse, perpetrators, and government authorities. Although it will be possible by institutionalizing infant self-esteem, depression, and anxiety tests with clues that depression and anxiety appear due to a decrease in self-concept in the characteristics of children affected by child abuse. We suggest that continuous progress of big data collection and analysis and algorithm development research to prevent child abuse, and expects that effective policies to prevent child abuse will be realized to eradicate child abuse crimes.

• Korean Journal of Microbiology
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• v.55 no.3
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• pp.268-273
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• 2019

The intestinal microbiomes vary according to the factors such environment, age and diet. The purpose of this study was to compare the gut microbial diversity between Korean infants receiving breast-fed milk and formula-fed milk. We analyzed microbial communities in stool samples collected from 80 Korean infants using next generation sequencing. Phylum level analysis revealed that microbial communities in both breast-fed infants group (BIG) was dominated by Actinobacteria ($74.22{\pm}3.48%$). Interestingly, the phylum Actinobacteria was dominant in formula-fed infants group A (FIG-A) at $73.46{\pm}4.12%$, but the proportions of phylum Actinobacteria were lower in formulafed infants group B and C (FIG-B and FIG-C) at $66.52{\pm}5.80%$ and $68.88{\pm}4.33%$. The most abundant genus in the BIG, FIG-A, FIG-B, and FIG-C was Bifidobacterium, comprising $73.09{\pm}2.31%$, $72.25{\pm}4.93%$, $63.81{\pm}6.05%$, and $67.42{\pm}5.36%$ of the total bacteria. Furthermore, the dominant bifidobacterial species detected in BIG and FIG-A was Bifidobacterium longum at $68.77{\pm}6.07%$ and $66.85{\pm}4.99%$ of the total bacteria. In contrast, the proportions of B. longum of FIG-B and FIG-C were $58.94{\pm}6.20%$ and $61.86{\pm}5.31%$ of the total bacteria. FIG-A showed a community similar to BIG, which may be due to the inclusion of galactooligosaccharide, galactosyllactose, synergy-oligosaccharide, bifidooligo and improvement material of gut microbiota contained in formula-milk. We conclude that 5-Bifidus factor contained in milk powder promotes the growth of Bifidobacterium genus in the intestines.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.75-80
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• 2020

Since the Fourth Industrial Revolution, important technologies such as big data analysis, robotics, Internet of Things, and the artificial intelligence have been used in various fields. Generally speaking it is understood that the big-data technology consists of gathering stage for enormous data, analyzing and processing stage and distributing stage. Until now crime records which is one of useful big-sized data are utilized to obtain investigation information after occurring crimes. If crime records are utilized to predict crimes it is believed that crime occurring frequency can be lowered by processing big-sized crime records in big-data framework. In this research the design is proposed that the smart system can provide the users of smart devices crime occurrence probability by processing crime records in big-data analysis. Specifically it is meant that the proposed system will guide safer routes by displaying crime occurrence probabilities on the digital map in a smart device. In the experiment result for a smart application dealing with small local area it is showed that its usefulness is quite good in crime prevention.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.10-16
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• 2021

As the implementation of smart factories spreads widely, the need for research to improve data efficiency is raised by prioritizing massive amounts of big data using IoT devices in terms of relevance and quality. The purpose of this study is to investigate whether prioritizing big data in management accounting decisions such as cost volatility estimation and recipe optimization can improve smart solution performance and decision-making effectiveness. Based on the survey answers of 84 decision makers at domestic small and medium-sized manufacturers who operate smart solutions such as ERP and MES that link manufacturing data in real time, empirical research was conducted. As a result, it was analyzed that setting prioritization of big data has a positive effect on decision-making in management accounting. became In addition, it was found that big data prioritization has a mediating effect that indirectly affects smart solution performance by using big data in management accounting decision making. Through the research results, it will be possible to contribute as a prior research to develop a scale to evaluate the correlation between big data in the process of business decision making.

• Journal of Platform Technology
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• v.11 no.4
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• pp.50-61
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• 2023

The advancement of various big data technologies has had a tremendous impact on many industries. Diverse big data research studies have been conducted to process and analyze massive data quickly. Under these circumstances, new emerging technologies such as high-capacity persistent memory (PMEM) and Compute Express Link (CXL) have lately attracted significant attention. However, little investigation into a big data "search" platform has been made. Moreover, most big data software platforms have been still optimized for traditional DRAM-based computing systems. This paper first evaluates the basic performance of Intel Optane PMEM, and then investigates both indexing and searching performance of Elasticsearch, a widely-known enterprise big data search platform, on the PMEM-based computing system to explore its effectiveness and possibility. Extensive and comprehensive experiments shows that the proposed Optane PMEM-based Elasticsearch achieves indexing and searching performance improvement by an average of 1.45 times and 3.2 times respectively compared to DRAM-based system. Consequently, this paper demonstrates the high I/O, high-capacity, and nonvolatile PMEM-based computing systems are very promising for big data search platforms.