• Proceedings of the Korean Information Science Society Conference
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• 2012.06c
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• pp.89-91
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• 2012

유망 주체의 선정은 기업협력 및 경쟁 관계에 있어 매우 중요하며 연구, 정부정책 및 기업전략의 수립에 있어 반드시 필요한 일이나 엄청나게 많은 정보의 양으로 인하여 많은 노력과 시간이 소요된다. 따라서 본 논문에서는 객관적으로 문헌 빅데이터를 분석하고 이를 통해 유망 주체를 선정해 내기 위한 통계적 문헌 분석 기반의 유망주체 선정 모델을 제안한다. 유망주체 선정을 위해서는 다양한 자질값들을 분석하여 기술 및 주체에 대한 통합 자질값을 구하고 이를 유망주체 선정에 활용한다. 또한 유망주체 선정에 세가지 기준(주체의 비전, 실행력, 활동력)을 통계적으로 분석하여 최종적으로 유망주체를 선정한다.

• The Journal of Bigdata
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• v.2 no.2
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• pp.67-74
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• 2017

The use of Bigdata plays an important role in all areas of society. Especially in the health care field, the role of Bigdata is very considerable because it deals with people's life and health. However, the interest and awareness of quality control of medical data is markedly low. Because the low-quality medical Bigdata leads to national loss and public health impairment, quality control of medical Bigdata is needed. The purpose of this research is to present the direction of medical Bigdata quality management by examining literature and cases of domestic and foreign medical Bigdata quality management practices. In addition, as a case of medical Bigdata quality control in the Y medical institution in Korea, activities of a Bigdata quality management TFT and results of a survey conducted for major data users in the hospital were presented.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.6
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• pp.681-689
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• 2019

The 46th Davos Forum of the World Economic Forum (WEF) predicts the continued growth of the 4th industry in the future. Currently, the 4th industry is attracting attention in various academic and practical fields. As a core technology of the 4th industry, Big Data is regarded as a major resource to lead the 4th industrial revolution along with artificial intelligence. As the growing interest in Big Data, researches on it are actively being done. However, literature studies on existing Big Data are focused on qualitative research, and quantitative research is insufficient. Therefore, this study aims to analyze the big data research flow in MIS field and to make academic thirst for quantification. This study has collected 145 abstracts of big data papers published in major journals in MIS field and confirmed that a majority of papers are published in Decision Support Systems Journal. Text mining and text network analysis were performed only for DSS journals to eliminate bias. As a result of the analysis, it was found out that researches on combining big data in the management field between 2012 and 2014, and researches on system development and analysis method for using big data from 2015 to 2017 were conducted.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.105-115
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• 2021

Many services are provided through big data analysis in various fields such as individuals, private sectors, and governments. There is a growing interest in training data scientists to provide these services. Particularly, interest in big data-based marketing curriculum is high. This study analyzed the domestic and foreign university big data-based marketing-related curriculum to utilize vast and diverse types of information from a marketing perspective in the era of big data. As a result of the analysis of 3,523 subjects related to digital marketing, big data marketing, data analysis, and developers collected according to the analysis criteria, it was analyzed that the specialized curriculum for training data scientists required in the era of the fourth industrial revolution was not appropriate. It is expected that the proposed curriculum in this study will be useful for the development of digital marketing and big data-based marketing curriculum.

• Proceedings of the Korean Society for Information Management Conference
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• 2016.08a
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• pp.19-22
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• 2016

본 연구는 빅데이터의 처리 저장 등과 같은 기술적 측면이 아닌 분석 활용적 측면에 초점을 맞춰 관련 학문분야를 파악하고 분야 간 지적구조를 규명하고자 하였다. 연구 결과 빅데이터 관련 연구들이 주제분야에 따라 명백한 차이를 보이고 있음을 확인할 수 있었다. 주제범주 분석을 통해 공학 기술(34.60%), 사회과학(25.24%), 자연과학(23.14%), 의학 보건학(14.85%) 등은 관련 연구가 비교적 고르게 분포되어 있지만, 인문학(1.69%)과 농업과학(0.21%)은 연구가 미비함을 알 수 있었다. 네트워크 분석 결과 사회과학 분야(31.58%)에 비해 공학 및 자연과학 분야(68.42%)의 빅데이터 연구가 더 활발함을 확인할 수 있었다. 또한 공학 및 자연과학 분야 연구들은 다양한 주제분야를 다루는 반면 사회과학 분야에서는 아직 한정된 주제분야에서 연구가 진행되고 있음을 알 수 있었다.

• Journal of Cadastre & Land InformatiX
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• v.50 no.2
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• pp.101-115
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• 2020

In this paper, big data analysis method was used to find out global trends in autonomous driving and to derive activate spatial information services. The applied big data was used in conjunction with news articles and patent document in order to analysis trend in news article and patents document data in spatial information. In this paper, big data was created and key words were extracted by using LDA (Latent Dirichlet Allocation) based on the topic model in major news on autonomous driving. In addition, Analysis of spatial information and connectivity, global technology trend analysis, and trend analysis and prediction in the spatial information field were conducted by using WordNet applied based on key words of patent information. This paper was proposed a big data analysis method for predicting a trend and future through the analysis of the connection between the autonomous driving field and spatial information. In future, as a global trend of spatial information in autonomous driving, platform alliances, business partnerships, mergers and acquisitions, joint venture establishment, standardization and technology development were derived through big data analysis.

• 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 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.

• Proceedings of the Korean Society for Information Management Conference
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• 2013.08a
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• pp.47-50
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• 2013

빅데이터를 활용한 데이터 분석 기법 중 비정형 데이터 분석의 하나인 텍스트 마이닝 기법을 활용하여, 외국 학술지에 나타난 한국의 경제 분야 트렌드를 분석한다. 데이터베이스로 Web of Knowledge의 연구논문을 활용하였으며, 키워드 분석, 네트워크 분석, 토픽모델링 분석을 통해 연구 동향 및 지적구조를 파악하는 데 그 목적이 있다.

• Journal of the Korea Institute of Building Construction
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• v.22 no.3
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• pp.281-291
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• 2022

This study attempts to use big data to determine the indicators necessary for a fire risk assessment of buildings. Because most of the causes affecting the fire risk of buildings are fixed as indicators considering only the building itself, previously only limited and subjective assessment has been performed. Therefore, if various internal and external indicators can be considered using big data, effective measures can be taken to reduce the fire risk of buildings. To collect the data necessary to determine indicators, a query language was first selected, and professional literature was collected in the form of unstructured data using a web crawling technique. To collect the words in the literature, pre-processing was performed such as user dictionary registration, duplicate literature, and stopwords. Then, through a review of previous research, words were classified into four components, and representative keywords related to risk were selected from each component. Risk-related indicators were collected through analysis of related words of representative keywords. By examining the indicators according to their selection criteria, 20 indicators could be determined. This research methodology indicates the applicability of big data analysis for establishing measures to reduce fire risk in buildings, and the determined risk indicators can be used as reference materials for assessment.