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Study on the Application of Big Data Mining to Activate Physical Distribution Cooperation : Focusing AHP Technique
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물류공동화 활성화를 위한 빅데이터 마이닝 적용 연구 : AHP 기법을 중심으로

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Type
Academic journal
Author
Young-Hyun Pak (경남대학교) Jae-Ho Lee (성균관대학교) Kyeong-Woo Kim (양산발전연구원)
Journal
Korea Trade Research Association Korea Trade Review Vol.46 No.5 KCI Accredited Journals
Published
2021.10
Pages
65 - 81 (17page)

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Study on the Application of Big Data Mining to Activate Physical Distribution Cooperation : Focusing AHP Technique
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Abstract· Keywords

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The technological development in the era of the 4th industrial revolution is changing the paradigm of various industries. Various technologies such as big data, cloud, artificial intelligence, virtual reality, and the Internet of Things are used, creating synergy effects with existing industries, creating radical development and value creation. Among them, the logistics sector has been greatly influenced by quantitative data from the past and has been continuously accumulating and managing data, so it is highly likely to be linked with big data analysis and has a high utilization effect. The modern advanced technology has developed together with the data mining technology to discover hidden patterns and new correlations in such big data, and through this, meaningful results are being derived. Therefore, data mining occupies an important part in big data analysis, and this study tried to analyze data mining techniques that can contribute to the logistics field and common logistics using these data mining technologies.
Therefore, by using the AHP technique, it was attempted to derive priorities for each type of efficient data mining for logisticalization, and R program and R Studio were used as tools to analyze this. Criteria of AHP method set association analysis, cluster analysis, decision tree method, artificial neural network method, web mining, and opinion mining. For the alternatives, common transport and delivery, common logistics center, common logistics information system, and common logistics partnership were set as factors.
#Big Data Analytic #SCM #AHP #Physical Distribution Cooperation

Contents

Abstract
Ⅰ. 서론
Ⅱ. 빅데이터 마이닝
Ⅲ. 연구모형의 설계
Ⅳ. 연구 결과 및 분석
Ⅴ. 결론
References

References (14)

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Bre, Facundo / 2018 / Prediction of wind pressure coefficients on building surfaces using artificial neural networks / Energy and Buildings 158:1429~1441 google schola Hassanzadeh, Hamed / 2012 / Semantic web requirements through web mining techniques / International Journal of Computer Theory and Engineering 4(4):616~620 google schola 김승욱 / 2017 / 물류에서 빅데이터 분석의 활용을 위한 가치 모델 / 디지털융복합연구 15(9):167~178 dbpia Kingsford, Carl / 2008 / What are decision trees? / Nature biotechnology 26(9):1011~1013 google schola 이은정 / 2021 / 스마트 물류의 적용과 활용방안에 관한 연구 / 무역금융보험연구 22(1):69~84 dbpia

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