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.
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References
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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.
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Li, Q., Xu, Z., Wei, H., Yu, C., Wang, S. (2020). General Big Data Architecture and Methodology: An Analysis Focused Framework. In: Debruyne, C., et al. On the Move to Meaningful Internet Systems: OTM 2019 Workshops. OTM 2019. Lecture Notes in Computer Science(), vol 11878. Springer, Cham. https://doi.org/10.1007/978-3-030-40907-4_4
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DOI: https://doi.org/10.1007/978-3-030-40907-4_4
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