빅데이터 연구동향 분석: 토픽 모델링을 중심으로
Research Trends Analysis of Big Data: Focused on the Topic Modeling
2
조회
0
다운로드
26
인용
0
참고문헌
키워드
초록
The objective of this study is to examine the trends in big data. Research abstracts were extracted from 4,019 articles, published between 1995 and 2018, on Web of Science and were analyzed using topic modeling and time series analysis. The 20 single-term topics that appeared most frequently were as follows: model, technology, algorithm, problem, performance, network, framework, analytics, management, process, value, user, knowledge, dataset, resource, service, cloud, storage, business, and health. The 20 multi-term topics were as follows: sense technology architecture (T10), decision system (T18), classification algorithm (T03), data analytics (T17), system performance (T09), data science (T06), distribution method (T20), service dataset (T19), network communication (T05), customer & business (T16), cloud computing (T02), health care (T14), smart city (T11), patient & disease (T04), privacy & security (T08), research design (T01), social media (T12), student & education (T13), energy consumption (T07), supply chain management (T15). The time series data indicated that the 40 single-term topics and multi-term topics were hot topics. This study provides suggestions for future research.
Abstract
The objective of this study is to examine the trends in big data. Research abstracts were extracted from 4,019 articles, published between 1995 and 2018, on Web of Science and were analyzed using topic modeling and time series analysis. The 20 single-term topics that appeared most frequently were as follows: model, technology, algorithm, problem, performance, network, framework, analytics, management, process, value, user, knowledge, dataset, resource, service, cloud, storage, business, and health. The 20 multi-term topics were as follows: sense technology architecture (T10), decision system (T18), classification algorithm (T03), data analytics (T17), system performance (T09), data science (T06), distribution method (T20), service dataset (T19), network communication (T05), customer & business (T16), cloud computing (T02), health care (T14), smart city (T11), patient & disease (T04), privacy & security (T08), research design (T01), social media (T12), student & education (T13), energy consumption (T07), supply chain management (T15). The time series data indicated that the 40 single-term topics and multi-term topics were hot topics. This study provides suggestions for future research.
참고문헌 (10)
- 김창식, "관광분야 연구동향 분석: 토픽모델링과 시계열분석을 중심으로", 관광레저연구, 29(12), pp.25~39 (2017), (사)한국관광레저학회
- 김창식, "토픽모델링과 시계열회귀분석을 활용한 정보시스템분야 연구동향 분석", 디지털콘텐츠학회논문지, 18(6), pp.1143~1150 (2017), 한국디지털콘텐츠학회[DOI]
- 김태경, "텍스트마이닝을 이용한 정보보호 연구동향 분석", (사)디지털산업정보학회 논문지, 14(2), pp.19~25 (2018), (사)디지털산업정보학회[DOI]
- 박준석, "텍스트마이닝과 소셜네트워크분석 기법을 활용한 호텔분야 연구동향 분석", 관광레저연구, 28(9), pp.209~226 (2016), (사)한국관광레저학회
- 윤혜진, "Research Trends Investigation Using Text Mining Techniques: Focusing on Social Network Services", 디지털콘텐츠학회논문지, 19(3), pp.513~519 (2018), 한국디지털콘텐츠학회[DOI]
- 김남규, "텍스트 분석 기술 및 활용 동향", 한국통신학회논문지, 42(2), pp.471~492 (2017), 한국통신학회[DOI]
- Blei, D., "Latent Dirichlet Allocation", Journal of Machine Learning Research, 3, pp.993~1022 (2003)
- 박자현, "토픽모델링을 활용한 국내 문헌정보학 연구동향 분석", 정보관리학회지, 30(1), pp.7~32 (2013), 한국정보관리학회[DOI]
- 배정환, "토픽 모델링을 이용한 트위터 이슈 트래킹 시스템", 지능정보연구, 20(2), pp.109~122 (2014), 한국지능정보시스템학회
- 양승준, "토픽모델링 기반 행복과 불행 이슈 분석 및 행복 증진 방안 연구", 지식경영연구, 17(2), pp.165~185 (2016), 한국지식경영학회[DOI]
