• Journal of Digital Convergence
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• v.14 no.12
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• pp.155-164
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• 2016

In this paper, we investigate and analyze big data platform published at home and abroad. The results had a problem with personal information security on each platform. In particular, there was a vulnerability in the encryption of personal information stored in big data representative of HBase NoSQL DB that is commonly used for big data platform. However, data encryption and decryption cause the system load. In this paper, we propose a method of encryption with HBase, encryption and decryption systems, and methods for applying the personal information management system (PMIS) for each step of the way and big data platform to reduce the load on the network to communicate. And we propose a new big data platform that reflects this. Therefore, the proposed Big Data platform will greatly contribute to the activation of Big Data used to obtain personal information security and system performance efficiency.

• 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.

• The Journal of Bigdata
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• v.4 no.2
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• pp.93-103
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• 2019

In this paper, we introduce a big data platform and a metadata management technique for fishery science R & D information. The big data platform collects and integrates various types of fisheries science R & D information and suggests how to build it in the form of a data lake. In addition to existing data collected and accumulated in the field of fisheries science, we also propose to build a big data platform that supports diverse analysis by collecting unstructured big data such as satellite image data, research reports, and research data. Next, by collecting and managing metadata during data extraction, preprocessing and storage, systematic management of fisheries science big data is possible. By establishing metadata in a standard form along with the construction of a big data platform, it is meaningful to suggest a systematic and continuous big data management method throughout the data lifecycle such as data collection, storage, utilization and distribution.

• Smart Media Journal
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• v.9 no.2
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• pp.99-109
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• 2020

This paper proposes a platform construction plan for the use of spectrum big data, collects and analyzes the big data in the radio wave field, establishes a linkage plan, and presents a support system scheme for linking and using the spectrum and public sector big data. It presented a plan to build a big data platform in connection with the spectrum public sector. In a situation where there is a lack of a support system for systematic analysis and utilization of big data in the field of radio waves, by establishing a platform construction plan for the use of big data by radio-related industries, the preemptive response to realize the 4th Industrial Revolution and the status and state of the domestic radio field. The company intends to contribute to enhancing the convenience of users of the big data platform in the public sector by securing the innovation growth engine of the company and contributing to the fair competition of the radio wave industry and the improvement of service quality. In addition, it intends to contribute to raising the social awareness of the value of spectrum management data utilization and establishing a collaboration system that uses spectrum big data through joint use of the platform.

• Information and Communications Magazine
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• v.29 no.11
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• pp.43-47
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• 2012

인터넷의 활성화 및 모바일 서비스의 등장으로 빅데이터 시대를 맞이하게 되었다. 이전에는 저장 및 처리할 수 없었던 영역. 이제는 새로운 기술의 등장과 분석을 통한 가치 창출의 가능성으로 빅데이터는 IT 업계의 최대 화두가 되어 가고 있다. 이러한 빅데이터를 바라보는 시각은 크게 기술적 관점과 분석적 관점으로 나뉘고 있다. 특히 기술적 관점에서 바라보는 빅데이터는 하둡을 표준으로 하는 오픈소스 분석 플랫폼의 대두가 고무적이다. 누구나가 대용량의 확장 가능한 시스템을 운영할 수 있는 기회가 온 것이다. 본 고에서는 빅데이터의 그 태생적 특징을 살펴보고, 비교적 저렴한 비용의 플랫폼 환경 구축을 위해 오픈소스 하둡이 널리 활용되고 있는 이유에 대해 알아본다. 또한 하둡의 용도와 어떠한 종류의 데이터 분석을 위해 사용되어지고 있는지, 그리고 하둡의 구성 및 하둡 생태계를 이루고 있는 요소들이 무엇인지 살펴본다. 끝으로 빅데이터를 활용하기 위한 6단계 절차와 이에 발맞춰 하둡 플랫폼을 어떻게 효율적으로 활용할 지에 대해 그 방법을 모색해 보고자 한다.

• Journal of the Korean Society for information Management
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• v.37 no.3
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• pp.25-50
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• 2020

Since big data platform services for the public library began January 1, 2016, libraries have used big data to improve their work performance. This paper aims to examine the use cases of library big data and attempts to draw improvement plan to improve the effectiveness of library big data. For this purpose, first, we examine big data used while utilizing the library big data platform, the usage pattern of big data and services/policies drawn by big data analysis. Next, the limitations and advantages of the library big data platform are examined by comparing the data analysis of the integrated library management system (ILUS) currently used in public libraries and data analysis through the library big data platform. As a result of case analysis, big data usage patterns were found program planning and execution, collection, collection, and other types, and services/policies were summarized as customizing bookshelf themes for the book curation and reading promotion program, increasing collection utilization, and building a collection based on special topics. and disclosure of loan status data. As a result of the comparative analysis, ILUS is specialized in statistical analysis of library collection unit, and the big data platform enables selective and flexible analysis according to various attributes (age, gender, region, time of loan, etc.) reducing analysis time. Finally, the limitations revealed in case analysis and comparative analysis are summarized and suggestions for improvement are presented.

• Knowledge Management Research
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• v.21 no.4
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• pp.243-263
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• 2020

The purpose of this study is to propose the performance measurement model for open big data platform. In order to develop the performance measurement model, we have integrated big data reference architecture(NIST 2018) with performance prism model(Neely et al. 2001) in the platform perspective of open big data. Our proposed model consists of five key building blocks for measuring performance of open data platform as follows: stakeholder contribution, big data governance capabilities, big data service capabilities, big data IT capabilities, and stakeholder satisfaction. In addition, our proposed model have twenty four evaluation indices and seventy five measurement items. We believe that our model could offer both research and practical implications for relevant research.

• The Journal of Bigdata
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• v.5 no.1
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• pp.97-108
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• 2020

As the domestic electric power industry becomes a smart grid, big data platforms for demand management, facility management, and customer service have been deployed. However, due to the nature of the big data project, big data platforms take time to realize their value in the business processes. Therefore, it is not easy to evaluate the performance of the initial big data platforms using the known or theoretical evaluation methods. In this paper, we propose a methodology of big data platform performance evaluation based on specific information quality such as information completeness/sufficiency, information reliability, information relevancy, information comparability, information unbiasedness, timeliness of information, related to the volume, diversity, and velocity of big data.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.535-536
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• 2021

빅데이터를 활용한 기계학습 모델을 개발하기 위해서는 빅데이터 처리를 위한 플랫폼과 딥러닝 프레임 워크 등 고급 분석을 수행할 수 있는 도구의 활용이 동시에 요구된다. 그러나 빅데이터 플랫폼과 딥러닝 프레임워크를 자유롭게 활용하기 위해서는 상당한 수준의 기술적 지식과 경험이 필요하다. 또한 빅데이터를 이용한 딥러닝 모델을 개발할 경우 분산처리와 병렬처리에 대한 지식과 추가적인 작업이 요구된다. 본 연구에서는 빅데이터를 활용한 기계학습 모형을 자유롭게 개발 및 공유하고 분산 딥러닝을 위한 시스템적 지원을 통해 분야별로 딥러닝 모형을 개발하는 응용 연구자들이 활용할 수 있는 플랫폼을 제시하였다. 본 연구를 통해 다양한 분야의 연구자들이 자신의 데이터를 이용하여 모형을 개발할 경우 분산처리와 병렬처리를 위한 기술적 제약을 극복하고 보다 빠르고 효율적인 방법으로 모형을 개발하고 현업에 활용할 수 있을 것으로 기대한다.

• Proceedings of The KACE
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• 2017.08a
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• pp.63-64
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• 2017

정보 기술의 끊임없는 발전에 따라 광범위한 분야에서 방대한 양의 데이터가 발생하게 되면서 이를 처리하기 위한 빅 데이터에 대한 연구 및 교육이 활발히 진행되고 있다. 이를 위하여 데이터 분석 및 처리를 위한 고성능의 서버 및 분산 처리를 위한 다수의 컴퓨터가 필요하며 이는, 개인 혹은 저사양의 수업 환경에서 빅 데이터를 학습하는 데에 어려움을 겪게 한다. 때문에 가상 환경에서 원활한 빅 데이터 학습을 위한 클라우드 기반의 시스템이 필요하다. 이에 본 논문에서는, 빅 데이터 처리 기술의 하나인 Spark를 이용한 빅 데이터 플랫폼 구축에 대하여 기술한다.