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General Big Data Architecture and Methodology: An Analysis Focused Framework

  • Qing LiEmail author
  • Zhiyong Xu
  • Hailong Wei
  • Chao Yu
  • ShuangShuang Wang
Conference paper
  • 17 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11878)

Abstract

With the development of information technologies such as cloud computing, the Internet of Things, the mobile Internet, and wireless sensor networks, big data technologies are driving the transformation of information technology and business models. Based on big data technology, data-driven artificial intelligence technology represented by deep learning and reinforcement learning has also been rapidly developed and widely used. But big data technology is also facing a number of challenges. The solution of these problems requires the support of a general big data reference architecture and analytical methodology. Based on the General Architecture Framework (GAF) and the Federal Enterprise Architecture Framework 2.0 (FEAF 2.0), this paper proposes a general big data architecture focusing on big data analysis. Based on GAF and CRISP-DM (cross-industry standard process for data mining), the general methodology and structural approach of big data analysis are proposed.

Keywords

Big data Architecture framework Methodology Modelling 

Notes

Acknowledgements

This work is sponsored by the National Natural Science Foundation of China No. 61771281, the “New generation artificial intelligence” major project of China No. 2018AAA0101605, the 2018 Industrial Internet innovation and development project, and Tsinghua University initiative Scientific Research Program.

References

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Qing Li
    • 1
    Email author
  • Zhiyong Xu
    • 1
  • Hailong Wei
    • 1
  • Chao Yu
    • 1
  • ShuangShuang Wang
    • 1
  1. 1.Department of AutomationTsinghua UniversityBeijingPeople’s Republic of China

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