• The Journal of Bigdata
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• v.4 no.1
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• pp.119-127
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• 2019

This study explains how to solve engineering problems easily and efficiently by using cloud based big data platform. To do this, we propose a cloud based big data analysis platform. The application helps users easily create models for data analysis using cloud based big data analysis platform. Analytical models modeled using components are analyzed through an analysis engine. Our platform include pre-processing, analysis, and visualization algorithms required for data analysis. Finally, we show an application of effluent concentration in a sewage treatment process.

• 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.

• Journal of The Korean Association of Information Education
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• v.24 no.2
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• pp.201-214
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• 2020

Big data is gradually expanding in diverse fields, with changing the data-related legislation. Moreover it would be interest in big data education. However, it requires a high level of knowledge and skills in order to utilize Big Data and it takes a long time for education spends a lot of money for training. We study that in order to define Universal Big Data used to the industrial field in a wide range. As a result, we make the paradigm for Big Data education for college students. We survey to the professional the Big Data definition and the Big Data perception. According to the survey, the Big Data related-professional recognize that is a wider definition than Computer Science Big Data is. Also they recognize that the Big Data Processing dose not be required Big Data Processing Frameworks or High Performance Computers. This means that in order to educate Big Data, it is necessary to focus on the analysis methods and application methods of Universal Big Data rather than computer science (Engineering) knowledge and skills. Based on the our research, we propose the Universal Big Data education on the new paradigm.

• The Journal of Bigdata
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• v.2 no.1
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• pp.71-75
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• 2017

The topic of big data has gained attention from the industry and the academics, because of the revitalization of social network services. The purpose of this study is to analyze the application cases of big data according to the categories of U-City services. The result from this study is that inside and unstructured information is more applied than outside and structured information in order to generate big data.

• Korean Journal of Construction Engineering and Management
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• v.23 no.2
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• pp.54-64
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• 2022

Since the interest in big data is growing exponentially, various types of research and development in the field of big data have been conducted in the construction industry. Among various application areas, cost estimating can be a topic where the use of big data provides positive benefits. In order for firms to make efficient use of big data for estimating tasks, they need to establish a strategy based on the multifaceted analysis of internal and external environments. The objective of the study is to develop and propose a strategy of the use of big data for construction management(CM) firms' cost estimating tasks based on the SWOT analysis. Through the combined efforts of literature review, questionnaire survey, interviews and the SWOT analysis, the study suggests that CM firms need to maintain the current level of the receptive culture for the use of big data and expand incrementally information resources. It also proposes that they need to reinforce the weak areas including big data experts and practice infrastructure for improving the big data-based cost estimating.

• Journal of Advanced Research in Ocean Engineering
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• v.3 no.1
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• pp.32-40
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• 2017

Recently, the amount of data to be processed and the complexity thereof have been increasing due to the development of information and communication technology, and industry's interest in such big data is increasing day by day. In the shipbuilding and offshore industry also, there is growing interest in the effective utilization of data, since various and vast amounts of data are being generated in the process of design, production, and operation. In order to effectively utilize big data in the shipbuilding and offshore industry, it is necessary to store and process large amounts of data. In this study, it was considered efficient to apply Hadoop and R, which are mostly used in big data related research. Hadoop is a framework for storing and processing big data. It provides the Hadoop Distributed File System (HDFS) for storing big data, and the MapReduce function for processing. Meanwhile, R provides various data analysis techniques through the language and environment for statistical calculation and graphics. While Hadoop makes it is easy to handle big data, it is difficult to finely process data; and although R has advanced analysis capability, it is difficult to use to process large data. This study proposes a big data platform based on Hadoop for applications in the shipbuilding and offshore industry. The proposed platform includes the existing data of the shipyard, and makes it possible to manage and process the data. To check the applicability of the platform, it is applied to estimate the weights of offshore structure topsides. In this study, we store data of existing FPSOs in Hadoop-based Hortonworks Data Platform (HDP), and perform regression analysis using RHadoop. We evaluate the effectiveness of large data processing by RHadoop by comparing the results of regression analysis and the processing time, with the results of using the conventional weight estimation program.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1259-1273
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• 2012

As a society of knowledge and information has been developed rapidly, because of changing from web environment to ubiquitous environment, a lot of countries across the world as well as S. Korea for national information with electronic Government have a variety of changes with big data. So this study is about development for national information and e-government of S. Korea with public data as big data analysed by the application of scenario planning. And then this research focused on the strategy consulting of national information with e-Government of S. Korea for utilization of public data as big data analysed by the application of 'scenario planning' as a foresight method. As a result, the future policy for utilization of public data as big data for national information with electronic government of S. Korea is to further spur the development of technology for linked data with semantic web for 'understanding of machine' than 'understanding of man'.

• Journal of Korea Multimedia Society
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• v.18 no.8
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• pp.925-934
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• 2015

The frequency of natural disasters because of global warming is gradually increasing, risks of flooding due to typhoon and torrential rain have also increased. Among these causes, the roads are flooded by suddenly torrential rain, and then vehicle and personal injury are happening. In this respect, because of the possibility that immersion of a road may occur in a second, it is necessary to study the rapid data collection and quick response system. Our research proposes a big data analysis system based on the collected information and a variety of system information collection methods for searching flooded road areas by torrential rains. The data related flooded roads are utilized the SNS data, meteorological data and the road link data, etc. And the big data analysis system is implemented the distributed processing system based on the Hadoop platform.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.5
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• pp.125-133
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• 2016

Data processing capacity and speed are rapidly increasing due to the development of hardware and software in recent time. As a result, data usage is geometrically increasing and the amount of data which computers have to process has already exceeded five-thousand transaction per second. That is, the importance of Big Data is due to its 'real-time' and this makes it possible to analyze all the data in order to obtain accurate data at right time under any circumstances. Moreover, there are many researches about this as construction of smart factory with the application of Big Data is expected to have reduction in development, production, and quality management cost. In this paper, system using In-Memory Data Grid for high speed processing is implemented in semiconductor process which numerous data occur and improved performance is proven with experiments. Implemented system is expected to be possible to apply on not only the semiconductor but also any fields using Big Data and further researches will be made for possible application on other fields.