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Bulletin of Chinese Academy of Sciences (Chinese Version)

Thinking on New System for Big Data Technology

Keywords

big data, technology architecture, software system stack, new model, new paradigm, security and trustworthy

Document Type

Build and Strengthen China`s Information Tech-system

Abstract

In recent years, there are such significant improvements on the performance and efficiency of big data technology and system. As it is widely applied in various fields, big data has empowered industrial intelligence, and is the key step into the intelligent stage of information society. Therefore, we are facing greater challenges nowadays, such as the paradox of data flooding and high-value data lacking, the complexity and uncertainty of big data analysis, and the difficulty to balance the data on sharing and circulation, and trustworthiness and security. Moreover, these challenges will not only promote the innovation and change of big data technology, but also develop and establish a new technology system. With respect to the requirements of new architecture, new paradigm, new model and security and trustworthiness, this study proposes to build a new big data analyzing and processing system stack, explore the new paradigm of extracting big data value, and outlook on the pioneer applications as the traction to a broad range of fields.

First page

60

Last Page

67

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

References

1 李国杰, 程学旗. 大数据研究:未来科技及经济社会发展的重大战略领域——大数据的研究现状与科学思考. 中国科学院院刊, 2012, (6):647-657. Li G J, Cheng X Q. Research status and scientific thinking of big data. Bulletin of Chinese Academy of Sciences, 2012, (6):647-657. (in Chinese) 2 徐宗本, 唐年胜, 程学旗. 数据科学:它的内涵、方法、意义与发展. 北京:科学出版社, 2021. Xu Z B, Tang N S, Cheng X Q. Data Science:Its concept, method, meaning and development. Beijing:Science Press, 2021. (in Chinese) 3 Wing J M. Trustworthy AI. Communications of the ACM, 2020, 64(10):64-71. 4 梅宏. 大数据导论. 北京:高等教育出版社, 2018. Mei H. Introduction to Big Data. Beijing:Higher Education Press, 2018. (in Chinese) 5 de Assuncao M D, Da Silva Veith A, Buyya R. Distributed data stream processing and edge computing:A survey on resource elasticity and future directions. Journal of Network and Computer Applications, 2018, 103:1-17. 6 Mcgregor A. Graph stream algorithms:A survey. ACM SIGMOD Record, 2014, 43(1):9-20. 7 Senior A W, Evans R, Jumper J, et al. Improved protein structure prediction using potentials from deep learning. Nature, 2020, 577:706-710. 8 Deng J, Dong W, Socher R, et al. ImageNet:A large-scale hierarchical image database. IEEE Computer Vision and Pattern Recognition, 2009, (1):248-255. 9 Johnson A E W, Pollard T J, Shen L, et al. MIMIC-III, a freely accessible critical care database. Scientific data, 2016, 3(1):1-9. 10 Hu W, Fey M, Zitnik M, et al. Open Graph Benchmark:Datasets for Machine Learning on Graphs. Neural Information Processing Systems (NeurIPS), 2020. 11 Qiu X, Sun T, Xu Y, et al. Pre-trained models for natural language processing:A survey. Science China Technological Sciences, 2020, 63(10):1-26. 12 程学旗, 梅宏, 赵伟, 等. 数据科学与计算智能:内涵、范式与机遇. 中国科学院院刊, 2020, 35(12):1470-1481. Cheng X Q, Mei H, Zhao W, et al. Data science and computing intelligence:Concept, paradigm, and opportunities. Bulletin of Chinese Academy of Sciences, 2020, 35(12):1470-1481. (in Chinese) 13 Kleinberg J, Ludwig J, Mullainathan S, et al. Discrimination in the Age of Algorithms. Journal of Legal Analysis, 2018, 10:113-174. 14 Stoica I, Song D, Popa R A, et al. A Berkeley View of Systems Challenges for AI. 2017. (2017-10-16)[2021-11-10]. http://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-159.pdf.

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