• The Journal of Bigdata
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• v.7 no.2
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• pp.139-152
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• 2022

Engineering's work area for plants is a technical area that directly affects productivity, performance, and quality throughout the lifecycle from planning, design, construction, operation and disposal. Using the different types of data that occur to make decisions is important not only in the subsequent process but also in terms of cyclical cost reduction. However, there is a lack of systems to manage and analyze these integrated data. In this paper, we developed a knowledge base-based plant engineering analysis platform that can manage and utilize data. The platform provides a knowledge base that preprocesses previously collected engineering data, and provides analysis and visualization to use it as reference data in AI models. Users can perform data analysis through the use of prior technology and accumulated knowledge through the platform and use visualization in decision-support and systematically manage construction that relied only on experience.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.47-57
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• 2013

Big data analysis needs a new tool for decision making in view of data volume, speed, and variety. Many global IT enterprises are announcing a variety of Big data products with easy to use, best functionality, and modeling capability. Big data packages are defined as a solution represented by analytic tools, infrastructures, platforms including hardware and software. They can acquire, store, analyze, and visualize Big data. There are many types of products with various and complex functionalities. Because of inherent characteristics of Big data, selecting a best Big data package requires expertise and an appropriate decision making method, comparing the selection problem of other software packages. The objective of this paper is to suggest a decision making method for selecting a Big data package. We compare their characteristics and functionalities through literature reviews and suggest selection criteria. In order to evaluate the feasibility of adopting packages, we develop two Analytic Hierarchy Process(AHP) models where the goal node of a model consists of costs and benefits and the other consists of selection criteria. We show a numerical example how the best package is evaluated by combining the two models.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.11
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• pp.1249-1254
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• 2014

Big data based on numerous data made by the people are used in order to obtain useful information. We can obtain more useful information if it can apply machine learning techniques added deformation of human memory on the characteristics of the computer program. And big data is predicted by using these conclusions. Humans are used to remember similar data as an original data, so big data processing technology should reflect these human characteristics. In this study, this algorithm to provide the selectivity of information is proposed. This algorithm is the technology to reflect the above factors. This algorithm is selected the data with high selectivity to determine similar data based on the deformation characteristics of the data.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.181-191
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• 2018

With the development of IT, hacking crimes are becoming intelligent and refined. In Emergency response, Big data analysis in information security is to derive problems such as abnormal behavior through collecting, storing, analyzing and visualizing whole log including normal log generated from various information protection system. By using the full log data, including data we have been overlooked, we seek to detect and respond to the abnormal signs of the cyber attack from the early stage of the cyber attack. We used open-source ELK Stack technology to analyze big data like unstructured data that occur in information protection system, terminal and server. By using this technology, we can make it possible to build an information security control system that is optimized for the business environment with its own staff and technology. It is not necessary to rely on high-cost data analysis solution, and it is possible to accumulate technologies to defend from cyber attacks by implementing protection control system directly with its own manpower.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.337-338
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• 2021

본 논문에서는 스마트 콘택트 렌즈를 이용한 건강 관리 어플리케이션을 제안한다. 기존에 연구되고 있던 스마트 콘택트 렌즈에 있는 센서들을 통해서 렌즈 사용자의 생체 데이터를 수집하고 수집한 데이터를 수치화 하여 당뇨병, 녹내장, 간략한 스트레스 지수 까지 시각화 하여 보다 편하게 자가 진단하고 예방하는데 그 목적이 있다. 렌즈의 구성으로는 생체 데이터를 수집하는 센서는 포도당 농도 측정 센서, 안압 측정 센서, 코르티솔 호르몬 측정 센서가 있고, 어플과 연동하기 위한 통신 센서가 포함 된다. 이후 수집된 데이터를 수치화 한 후 어플 화면에 시각화하여 사용자가 각 항목을 확인하는데 있어서 편리 할 수 있다. 많은 사용자들의 데이터가 모이면 이를 빅데이터로 구성한 뒤 건강관련 부서와 연동하여 기존에 의사들이 더 정확한 진단을 할 수 있도록 지원한다. 또한 사용자들은 어플을 사용하면서 예방하는데 큰 목적을 둔다.

• The Korean Journal of Applied Statistics
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• v.34 no.2
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• pp.267-278
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• 2021

As time goes by, the amount of data is increasing regardless of government, business, domestic or overseas. Accordingly, research on big data is increasing in academia. Statistics is one of the major disciplines of big data research, and it will be interesting to understand the research trend of statistics through big data in the growing number of papers in statistics. In this study, we analyzed what studies are being conducted through abstract data of statistical papers in Korea and abroad. Research trends in domestic and international were analyzed through the frequency of keyword data of the papers, and the relationship between the keywords was visualized through the Word Embedding method. In addition to the keywords selected by the authors, words that are importantly used in statistical papers selected through Textrank were also visualized. Lastly, 10 topics were investigated by applying the LDA technique to the abstract data. Through the analysis of each topic, we investigated which research topics are frequently studied and which words are used importantly.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.861-868
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• 2021

With the development of technology for visualizing big-data, tasks such as intuitively analyzing a lot of data, detecting errors, and deriving meaning are actively progressing. In this paper, we describe the design and implementation of a 3D analysis that collects and stores the writing data in Chinese characters provided by the Korean Classical Database of the Korean Classics Translation Institute, stores and progress the data, and visualizes the writing information in a 3D network diagram. It solves the problem when a large amount of data is expressed in 2D, intuitive that analysis, error detection, meaningful data extraction such as characteristics, similarity, differences, etc. and user convenience can be provided. In this paper, we improved the problems of analyzing Chosun dynasty poetry in Chinese characters using 2D visualization conducted in previous studies.

• Annual Conference of KIPS
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• 2019.10a
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• pp.763-766
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• 2019

최근 사람들의 삶의 질이 향상됨에 따라 기호품인 와인의 수요가 늘어나고 있다. 그러나 와인은 생산하는데 길게는 수십 년이 걸리는 고가의 제품이므로 소비자가 와인과 잘못 구매했을 때의 기회비용이 크다. 본 논문에서는 전문 와인 테이스터 들의 후기 빅 데이터를 활용하여 딥러닝 기반 추천시스템을 개발을 다룬다. 테이스터 들의 후기 빅 데이터에 대해 Apache Pig와 자연어 처리를 통한 전 처리 과정을 수행해 리뷰 별로 특징 벡터를 구성하고, 하이퍼 매개변수 최적화와 조기 종료 기법을 사용해 데이터에 대하여 최적의 딥러닝 분류기를 구성하였다. 마지막으로, 구성된 시스템의 신뢰도를 검증하기 위해서 딥러닝의 정확도와 오차율을 확인하였고 시스템이 추천한 와인을 시각화 이미지와 비교하여 성능을 검증하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.7
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• pp.501-511
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• 2013

Recently, the visualization method of social network service have been only focusing on presentation of visualizing network data, which the methods do not consider an efficient processing speed and computational complexity for increasing at the ratio of arithmetical of a big data regarding social networks. This paper proposes a cloud based on visualization method to visualize a user focused hierarchy relationship between user's nodes on social network. The proposed method can intuitionally understand the user's social relationship since the method uses fuzzy to represent a hierarchical relationship of user nodes of social network. It also can easily identify a key role relationship of users on social network. In addition, the method uses hadoop and hive based on cloud for distributed parallel processing of visualization algorithm, which it can expedite the big data of social network.

• The Journal of Bigdata
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• v.4 no.1
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• pp.53-61
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• 2019

The extracellular matrix, which provides the structural and biochemical support of surrounding cells, is a cell physiological modulator that controls cell division and differentiation. In the bio sector, the company produces Scapold, a three-dimensional support for tissue engineering, and cultivates stem cells in the produced Scapold to be transplanted into animals to assess tissue regeneration. This depends on components such as collagen in the tissue. Therefore, it is very important to identify the inclusion rate and distribution of components in the tissue, and the data are obtained by analyzing the color of the dyed tissue image. The process from image collection to analysis is costly, and the data collected and analyzed are managed in different formats by different research institutions. Therefore, data integration management and analysis results search are not being performed. In this paper, we establish a database that can manage relevant bigdata in an integrated manner, and propose a bio-image integrated management and retrieval system that can be searched based on color, an important analytical measure in this field of study.