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Multi-Source Data Repairing: A Comprehensive Survey

Author

Listed:
  • Chen Ye

    (Hangzhou Dianzi University, Hangzhou 310018, China
    Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
    Jubang Group Co., Ltd., Yueqing 325600, China)

  • Haoyang Duan

    (Hangzhou Dianzi University, Hangzhou 310018, China)

  • Hengtong Zhang

    (Tencent AI Lab, Shenzhen 518054, China)

  • Hua Zhang

    (Hangzhou Dianzi University, Hangzhou 310018, China)

  • Hongzhi Wang

    (Harbin Institute of Technology, Harbin 150001, China)

  • Guojun Dai

    (Hangzhou Dianzi University, Hangzhou 310018, China)

Abstract

In the era of Big Data, integrating information from multiple sources has proven valuable in various fields. To ensure a high-quality supply of multi-source data, repairing different types of errors in the multi-source data becomes critical. This paper categorizes errors in multi-source data into entity information overlapping, attribute value conflicts, and attribute value inconsistencies. We first summarize existing repairing methods for these errors and then examine and review the study of the detection and repair of compound-type errors in multi-source data. Finally, we indicate further research directions in multi-source data repair.

Suggested Citation

  • Chen Ye & Haoyang Duan & Hengtong Zhang & Hua Zhang & Hongzhi Wang & Guojun Dai, 2023. "Multi-Source Data Repairing: A Comprehensive Survey," Mathematics, MDPI, vol. 11(10), pages 1-22, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2314-:d:1147931
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