• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.129-132
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• 2019

대학기관연구는 대학에서 이루어지는 다양한 의사결정을 체계적으로 지원하는 활동이다. 본 대학에서는 최근에 전공자와 비전공자 간의 교양과목 평가 분리에 대한 제도를 마련하였으나 이 결정을 교수자의 재량에 맡겨 놓아 근거 데이터 없이 교수자가 평가 분리 여부를 실제로 결정하기에는 어려운 실정이다. 이에 본 논문은 이전 학기의 성적 데이터를 기반으로 통계 분석한 결과와 이에 대한 시각화를 제공하여 교수자가 조금 더 쉽게 평가 분리 여부를 결정할 수 있도록 분석 시스템을 제안하고 실제 교양과목에 대한 분석 결과를 예시를 통해 보여준다.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1516-1529
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• 2016

While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.

• Journal of Wetlands Research
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• v.18 no.1
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• pp.100-112
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• 2016

In recent, the direction of water resources policy is changing from the typical plan for water use and flood control to the sustainable water resources management to improve the quality of life. This change makes the information related to water resources such as data collection, management, and supply is becoming an important concern for decision making of water resources policy. We had analyzed the structured data according to the purpose of providing information on water resources. However, the recent trend is big data and cloud computing which can create new values by linking unstructured data with structured data. Therefore, the trend for the management of water resources information is also changing. According to the paradigm change of information management, this study tried to suggest an application of big data and cloud computing in water resources field for efficient management and use of water. We examined the current state and direction of policy related to water resources information in Korea and an other country. Then we connected volume, velocity and variety which are the three basic components of big data with veracity and value which are additionally mentioned recently. And we discussed the rapid and flexible countermeasures about changes of consumer and increasing big data related to water resources via cloud computing. In the future, the management of water resources information should go to the direction which can enhance the value(Value) of water resources information by big data and cloud computing based on the amount of data(Volume), the speed of data processing(Velocity), the number of types of data(Variety). Also it should enhance the value(Value) of water resources information by the fusion of water and other areas and by the production of accurate information(Veracity) required for water management and prevention of disaster and for protection of life and property.

• Journal of Digital Convergence
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• v.14 no.9
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• pp.317-323
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• 2016

Big Data is now seen as a major asset in the company's competitiveness, its influence in the future is expected to grow. Companies that recognize the importance are already actively engaged with Big Data in product development and marketing, which are increasingly applied across sectors of society, including politics, sports. However, lack of knowledge of the system implementation and high costs are still a big obstacles to the introduction of Big Data and systems. It is an objective in this study to build a Big Data system, which is based on open source Hadoop and Hive among Big Data systems, utilizing POS sales data of small and medium-sized offline markets. This approach of convergence is expected to improve existing sales systems that have been simply focusing on profit and loss analysis. It will also be able to use it as the basis for the decisions of the executive to enable prediction of the consumption patterns of customer preference and demand in advance.

• 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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.405-414
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• 2017

Spatial data processing often poses challenges due to the unique characteristics of spatial data and this becomes more complex in spatial big data processing. Some tools have been developed and provided to users; however, they are not common for a regular user. This paper presents a benchmark test between two notable tools of spatial big data processing: GIS Tools for Hadoop and SpatialHadoop. At the same time, a MapReduce application is introduced to be used as a baseline to evaluate the effectiveness of two tools and to derive the impact of number of maps/reduces on the performance. By using these tools and New York taxi trajectory data, we perform a spatial data processing related to filtering the drop-off locations within Manhattan area. Thereby, the performance of these tools is observed with respect to increasing of data size and changing number of worker nodes. The results of this study are as follows 1) GIS Tools for Hadoop automatically creates a Quadtree index in each spatial processing. Therefore, the performance is improved significantly. However, users should be familiar with Java to handle this tool conveniently. 2) SpatialHadoop does not automatically create a spatial index for the data. As a result, its performance is much lower than GIS Tool for Hadoop on a same spatial processing. However, SpatialHadoop achieved the best result in terms of performing a range query. 3) The performance of our MapReduce application has increased four times after changing the number of reduces from 1 to 12.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.607-612
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• 2015

As the amount of vehicle's diagnostics data increases, the actors in automotive ecosystem will encounter difficulties to perform a real time analysis in order to simulate or to design new services according to the data gathered from the connected cars. In this paper, we have conducted a study of a Big Data solution that expresses the essential deep analytics to process and analyze vast quantities of vehicles on board diagnostics data generated by cars. Hadoop and its ecosystems have been deployed to process a large data and delivered useful outcomes that may be used by actors in automotive ecosystem to deliver new services to car owners. As the Intelligent transport system is involved to guarantee safety, reduce rate of crash and injured in the accident due to speed, addressing big data solution based on vehicle diagnostics data is upcoming to monitor real time outcome from it and making collection of data from several connected cars, facilitating reliable processing and easier storage of data collected.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.129-130
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• 2018

Big data analysis is a technique for effectively analyzing unstructured data such as the Internet, social network services, web documents generated in the mobile environment, e-mail, and social data, as well as well formed structured data in a database. The most big data analysis techniques are data mining, machine learning, natural language processing, and pattern recognition, which were used in existing statistics and computer science. Global research institutes have identified analysis of big data as the most noteworthy new technology since 2011. Therefore, companies in most industries are making efforts to create new value through the application of big data. In this study, we analyzed using the Social Matrics which a big data analysis tool of Daum communications. We analyzed public perceptions of "Artificial Intelligence" keyword, one month as of May 19, 2018. The results of the big data analysis are as follows. First, the 1st related search keyword of the keyword of the "Artificial Intelligence" has been found to be technology (4,122). This study suggests theoretical implications based on the results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2301-2306
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• 2015

The time to address the exabytes of data has come as the information age accelerates. Big data transfer technology is essential for processing large amounts of data. This paper posits to transfer big data in the optimal conditions by the proposed algorithm for predicting the optimal combination of Pipelining, Concurrency, and Parallelism (PCP), which are major functions of GridFTP. In addition, the author introduced a simple design process of Takagi-Sugeno-Kang (TSK) fuzzy model and designed a model for predicting transfer throughput with optimal combination of Pipelining, Concurrency and Parallelism. Hence, the author evaluated the model of the proposed algorithm and the TSK model to prove the superiority.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.258-260
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• 2013

There are two major factors to use NoSQL in order to manage Big Data; to increase productivity of an application programmer and to increase data access performance. But, in many business fields, this hopeful plan lacks careful consideration. For efficient and effective management and analysis of Big Data, it is necessary to perform a test with the expectation for productivity and performance of the application programmer before deciding whether NoSQL technique is used or not. In this paper, we research on programmer productivity, data access performance, risk distribution, and so forth.