디지털융복합연구 (Journal of Digital Convergence)

  • 제19권6호
  • /
  • Pages.259-264
  • /
  • 2021
  • /
  • 2713-6434(pISSN)
  • /
  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
권호 표지
DOI QR코드

DOI QR Code

빅데이터를 이용한 중고 거래 분석 시스템 연구

A Study for Used Transaction Analysis System using Big Data

  • 투고 : 2021.04.06
  • 심사 : 2021.06.20
  • 발행 : 2021.06.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

최근 중고 거래를 지원하는 중고 거래 사이트가 증가함에 따라 사용자는 실시간으로 다양한 정보를 검색하고자 한다. 이러한 새로운 변화는 전자상거래 기반에서 새로운 유형의 C2C(Commerce to Commerce) 거래가 활성화 되었다. 그러나 각각의 중고 거래 사이트는 고유한 특성들이 있어 전체 표준화가 어렵다. 따라서 본 논문에서는 사용자가 사용한 거래 데이터를 실시간으로 제공하고 원하는 정보를 신속하게 제공하는 시스템을 연구하였다. 본 논문에서는 인터넷 전자 상거래를 통한 중고품 통합 거래 시스템 개발에 필요한 크롤러 시스템을 연구하고, 정의된 형태소 분석기를 통해 사용자가 원하는 웹 환경에서 정보 제공이 가능하도록 하였다. 따라서 본 연구에서는 다양한 중고 물품 사이트에 접속하지 않고도 사용자가 원하는 정보를 제공하는 시스템을 설계하였다.

Recently, as the number of used trading sites supporting used trading increases, users want to search for a variety of information in real time. This new change has enabled a new type of C2C (Commerce to Commerce) transaction in the e-commerce base. However, since each used trading site has its own characteristics, it is difficult to standardize the whole. Therefore, in this paper, we studied a system that provides the transaction data used by the user in real time and provides the desired information quickly. In this paper, we researched the crawler system necessary for the development of the integrated trading system for used goods through Internet e-commerce, and made it possible to provide information in the web environment desired by the user through the defined morpheme analyzer. Therefore, in this study, we designed a system that provides information desired by users without accessing various used goods sites.

키워드

참고문헌

  1. R. J. Gordon. (2020). Does the new economy measure up to the great inventions of the past? J. Econ. Perspect, 14, 49-74. https://doi.org/10.1257/jep.14.4.49
  2. B. Carlsson. (2019). The Digital Economy: What Is New and What Is Not? Struct. Chang. Econ. Dyn, 15, 245-264. https://doi.org/10.1016/j.strueco.2004.02.001
  3. R. Armagan, Yeni Ekonomi ve Turkiye. Suleyman Demirel Universitesi IIBF Dergisi 2000, 5, 139-153.
  4. Akyazi, H.; Kalca, A. Yeni Ekonomi ve Iktisat Bilimi. Liberal Dusunce Dergisi 2019, 29, 221-242.
  5. BARISIK, S.; Yirmibescik, O. Turkiye'de Yeni Ekonomi'nin Olusum Surecini Hizlandirmaya Yonelik Uyum Cabalari. ZKU Sosyal Bilimler Dergisi 2020, 2, 39-62.
  6. Viskari, S.; Pekka, S.; Marko, T. Implementation of Open Innovation Paradigm, Cases: Cisco Systems, Dupont, IBM, Intel, Lucent, P&G, Philips and Sun Microsystems; Lappeenranta University of Technology Research Report 189; Lappeenranta University of Technology: Lappeenranta, Finland, 2020.
  7. Conboy, K.; Mikalef, P.; Dennehy, D.; Krogstie, J. Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda. Eur. J. Oper. Res. 2020, 281, 656-672. https://doi.org/10.1016/j.ejor.2019.06.051
  8. Mikalef, P.; Boura, M.; Lekakos, G.; Krogstie, J. Big data analytics capabilities and innovation: The mediating role of dynamic capabilities and moderating effect of the environment. Br. J. Manag. 2019, 30, 272-298. https://doi.org/10.1111/1467-8551.12343
  9. Taylor, T. Thinking about a new economy. Public Interest 2001, 24, 3-19.
  10. Addo-Tenkorang, R.; Helo, P.T. Big data applications in operations/supplychain management: A literature review. Comput. Ind. Eng. 2020, 101, 528-543. https://doi.org/10.1016/j.cie.2016.09.023
  11. Huang, B.; Jin, L.; Lu, Z.; Yan, M.; Wu, J.; Hung, P.C.; Tang, Q. RDMA-driven MongoDB: An approach of RDMA enhanced NoSQL paradigm for large-Scale data processing. Inf. Sci. 2019, 502, 376-393, doi:10.1016/j.ins.2019.06.048.
  12. Schaffer, E.; Mayr, A.; Fuchs, J.; Sjarov, M.; Vorndran, J.; Franke, J. Microservice-based architecture for engineering tools enabling a collaborative multi-user configuration of robot-based automation solutions. Procedia CIRP 2019, 86, 86-91. https://doi.org/10.1016/j.procir.2020.01.017
  13. Fabian, K.; Philipp, B. Return of the JS: Towards a Node.js-Based Software Architecture for Combined CMS/CRM Applications. Procedia Comput. Sci. 2020, 141, 454-459. https://doi.org/10.1016/j.procs.2018.10.143
  14. Boran, F.E. Genc, S.; Kurt, M.; Akay, D. A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Syst. Appl. 2021 36, 11363-11368. https://doi.org/10.1016/j.eswa.2009.03.039
  15. T. J. Barker & Z. B. Zabinsky. (2011). A multicriteria decision making model for reverse logistics using analytical hierarchy process. Omega, 39(5), 558-573. https://doi.org/10.1016/j.omega.2010.12.002
  16. Zheng Xu. (2017). The analytics and applications on supporting big data framework in wireless surveillance networks, International Journal of Social and Humanistic Computing, Volans 2(3), 141-149. https://doi.org/10.1504/IJSHC.2017.084732