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A Review of Data Fusion Techniques for Government Big Data

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Big Data (BigData 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1496))

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Abstract

Nowadays, the development of e-government has ushered in the era of big data. Data is playing an increasingly important role in the government’s social management and public services. Government big data refers to various information resources such as documents, forms, or charts generated or obtained by government departments in the process of performing their duties. Government data is large in scale, various sourced, and diverse, so there are some difficulties in how to effectively fuse and analyze the complex government data to gain accurate decisions under the premise of ensuring key information will be preserved and key features will be taken seriously. Starting from the definition of government data, this paper firstly analyzed the meaning and the characteristics of government data, and problems in dealing with government data. Then discussed data fusion framework and techniques for government data in three directions: data level, feature level, and decision level. Finally, summarized existing technology and put forward problems that needed to be studied in the future.

Y. Yang, J. Guo, C. Liu, J. Liu, S. Chen, X. Ning—These authors contributed equally to this work.

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Author information

Authors and Affiliations

  1. China Academic of Electronics and Information Technology, Shuangyuan Street, Beijing, 100041, China

    Bo Zhang, Yunxiang Yang, Jing Guo, Chenshen Liu, Junjie Liu, Siyuan Chen & Xiaobin Ning

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  1. Bo Zhang
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  2. Yunxiang Yang
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  3. Jing Guo
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  4. Chenshen Liu
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  5. Junjie Liu
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  6. Siyuan Chen
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  7. Xiaobin Ning
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Editor information

Editors and Affiliations

  1. National University of Defense Technology, Changsha, China

    Xiangke Liao

  2. Shenzhen University of Technology, Chinese Academy of Sciences, Shenzhen, China

    Wei Zhao

  3. University of Science and Technology of China, Hefei, China

    Enhong Chen

  4. Sun Yat-sen University, Guangzhou, China

    Nong Xiao

  5. Taiyuan University of Technology, Taiyuan, China

    Li Wang

  6. Nanjing University, Nanjing, China

    Yang Gao

  7. Nanjing University, Nanjing, China

    Yinghuan Shi

  8. Sun Yat-sen University, Guangzhou, China

    Changdong Wang

  9. Sun Yat-sen University, Guangzhou, China

    Dan Huang

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Cite this paper

Zhang, B. et al. (2022). A Review of Data Fusion Techniques for Government Big Data. In: Liao, X., et al. Big Data. BigData 2022. Communications in Computer and Information Science, vol 1496. Springer, Singapore. https://doi.org/10.1007/978-981-16-9709-8_4

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  • DOI: https://doi.org/10.1007/978-981-16-9709-8_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-9708-1

  • Online ISBN: 978-981-16-9709-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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