Multi-Source Data Repairing: A Comprehensive Survey

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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.
Funding Information
  • National Natural Science Foundation of China (62202132, LQ22F020032, 2022YFE0199300)
  • Natural Science Foundation of Zhejiang Province (62202132, LQ22F020032, 2022YFE0199300)
  • National Key Research and Development Program of China (62202132, LQ22F020032, 2022YFE0199300)

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