• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.54-63
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• 2016

As technology has developed and cost for data processing has reduced, big data market has grown bigger. Developed countries such as the United States have constantly invested in big data industry and achieved some remarkable results like improving advertisement effects and getting patents for customer service. Every company aims to achieve long-term survival and profit maximization, but it needs to establish a good strategy, considering current industrial conditions so that it can accomplish its goal in big data industry. However, since domestic big data industry is at its initial stage, local companies lack systematic method to establish competitive strategy. Therefore, this research aims to help local companies diagnose their big data capabilities through a reference model and big data capability assessment system. Big data reference model consists of five maturity levels such as Ad hoc, Repeatable, Defined, Managed and Optimizing and five key dimensions such as Organization, Resources, Infrastructure, People, and Analytics. Big data assessment system is planned based on the reference model's key factors. In the Organization area, there are 4 key diagnosis factors, big data leadership, big data strategy, analytical culture and data governance. In Resource area, there are 3 factors, data management, data integrity and data security/privacy. In Infrastructure area, there are 2 factors, big data platform and data management technology. In People area, there are 3 factors, training, big data skills and business-IT alignment. In Analytics area, there are 2 factors, data analysis and data visualization. These reference model and assessment system would be a useful guideline for local companies.

• Journal of Information Technology Applications and Management
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• v.20 no.4
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• pp.1-18
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• 2013

Big data has become a worldwide topic. Despite this, big data accurately understand and acquire the business to take advantage of companies that were only very few. The purpose of this study is to investigate the factors that effect Korean firm's adopting big data system. Empirical test was conducted to verify hypotheses using extended technology acceptance model and we analyzed factors which affect the behavioral intention of big data System. Based upon previous researches, we have selected organization innovation, organization slank, organization information system infra maturity, perceived benefits of big data system, perceived usefulness, perceived ease of use, behavioral intention as variables and proposed a research model based on survey questionnaires. From those, we drew that perceived usefulness and perceived ease of use influenced the behavioral intention. The results of this study will increase the users' awareness on big data system and contribute to develop a way to enable the introduction of new technologies.

• Spatial Information Research
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• v.21 no.5
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• pp.43-51
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• 2013

In this study, the concept of spatial big data and effective ways to build a spatial big data system are presented. Big Data is defined as 3V(volume, variety, velocity). Spatial big data is the basis for evolution from 3V's big data to 6V's big data(volume, variety, velocity, value, veracity, visualization). In order to build an effective spatial big data, spatial big data system building should be promoted. In addition, spatial big data system should be performed a national spatial information base, convergence platform, service providers, and providers as a factor of production. The spatial big data system is made up of infrastructure(hardware), technology (software), spatial big data(data), human resources, law etc. The goals for the spatial big data system build are spatial-based policy support, spatial big data platform based industries enable, spatial big data fusion-based composition, spatial active in social issues. Strategies for achieving the objectives are build the government-wide cooperation, new industry creation and activation, and spatial big data platform built, technologies competitiveness of spatial big data.

• Journal of Engineering Education Research
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• v.22 no.5
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• pp.37-48
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• 2019

The government has tried to develop a platform for systematically collecting and managing engineering education data for policy proposals. However, there have been few cases of big data analysis platform for policy proposals in engineering education, and it is difficult to determine the major function of the platform, the purpose of using big data, and the method of data collection. This study aims to collect the cases of big data analysis systems for the development of a big data system for educational policy proposals, and to conduct a study to analyze cases using the analysis frame of key elements to consider in developing a big data analysis platform. In order to analyze the case of big data system for engineering education policy proposals, 24 systems collecting and managing big data were selected. The analysis framework was developed based on literature reviews and the results of the case analysis were presented. The results of this study are expected to provide from macro-level such as what functions the platform should perform in developing a big data system and how to collect data, what analysis techniques should be adopted, and how to visualize the data analysis results.

• KIISE Transactions on Computing Practices
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• v.24 no.1
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• pp.24-32
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• 2018

This paper proposes a Big Data system for energy Big Data which is aggregated in real-time from industrial and public sources. The constructed Big Data system is based on Hadoop and the Spark framework is simultaneously applied on Big Data processing, which supports in-memory distributed computing. In the paper, we focus on Big Data, in the form of heat energy for district heating, and deal with methodologies for storing, managing, processing and analyzing aggregated Big Data in real-time while considering properties of energy input and output. At present, the Big Data influx is stored and managed in accordance with the designed relational database schema inside the system and the stored Big Data is processed and analyzed as to set objectives. The paper exemplifies a number of heat demand plants, concerned with district heating, as industrial sources of heat energy Big Data gathered in real-time as well as the proposed system.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.217-224
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• 2019

As the world's population grows, how to maintain the food supply is becoming a bigger problem. Now and in the future, big data will play a major role in decision making in the agriculture industry. The challenge is how to obtain valuable information to help us make future decisions. Big data helps us to see history clearer, to obtain hidden values, and make the right decisions for the government and farmers. To contribute to solving this challenge, we developed the Agriculture Big Data Analysis System. The system consists of agricultural big data collection, big data analysis, and big data visualization. First, we collected structured data like price, climate, yield, etc., and unstructured data, such as news, blogs, TV programs, etc. Using the data that we collected, we implement prediction algorithms like ARIMA, Decision Tree, LDA, and LSTM to show the results in data visualizations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.8
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• pp.1111-1115
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• 2014

With the increasing interest of a demand side management using a Smart Grid infrastructure, the demand resources and energy usage data management becomes an important factor in energy industry. In addition, with the help of Advanced Measuring Infrastructure(AMI), energy usage data becomes a Big Data System. Therefore, it becomes difficult to store and manage the demand resources big data using a traditional relational database management system. Furthermore, not many researches have been done to analyze the big energy data collected using AMI. In this paper, we are proposing a Hadoop based Big Data system to manage the demand resources energy data and we will also show how the demand side management systems can be used to improve energy efficiency.

• Spatial Information Research
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• v.23 no.6
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• pp.19-29
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• 2015

This study was conducted with the purpose of suggesting the improvement plan of political for activating the Geo-Spatial Big Data Services. To this end, we were review the previous research for Geo-Spatial Big Data and analysis domestic and foreign Geo-Spatial Big Data propulsion system and policy enforcement situation. As a result, we have deduced the problem of insufficient policy of reaction for future Geo-Spatial Big Data, personal information protection and political basis service activation, relevant technology and policy, system for Geo-Spatial Big Data application and establishment, low leveled open government data and sharing system. In succession, we set up a policy direction for solving derived problems and deducted 5 policy issues : setting up a Geo-Spatial Big Data system, improving relevant legal system, developing technic related to Geo-Spatial Big Data, promoting business supporting Geo-Spatial Big Data, creating a convergence sharing system about public DB.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.217-221
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• 2017

Proactive prevention is important for crime. Past crimes have focused on coping after death and punishing them. But with Big Data technology, crime can be prevented spontaneously. Big data can predict the behavior of criminals or potential criminals. This article discusses how to build a big data system for crime prevention. Specifically, it deals with the way to combine unstructured data of big data with basic form data, and as a result, designs crime prevention system. Through this study, it is expected that the possibility of using big data for crime prevention is described through fingerprints, and it is expected to help crime prevention program and research in future.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.143-149
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• 2021

As the need to establish a big data system for rapid provision of big data and efficient management of resources has emerged due to rapid entry into the hyper-connected intelligence information society, public institutions are pushing to establish a big data system. Therefore, this study analyzed and combined the success factors of big data-related studies and the specific aspects of big data in public institutions based on the measurement of environmental factors for establishing an integrated information system for higher education institutions. In addition, 19 measurement items reflecting big data characteristics were derived from big data experts using brainstorming and Delphi methods, and a plan to successfully apply them to public institutions that want to build big data systems was proposed. We hope that this research results will be used as a foundation for the successful establishment of big data systems in public institutions.