Multi-Source Data Repairing: A Comprehensive Survey

Chen Ye, Haoyang Duan, Hengtong Zhang, Hua Zhang (), Hongzhi Wang and Guojun Dai ()
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Chen Ye: Hangzhou Dianzi University, Hangzhou 310018, 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

Mathematics, 2023, vol. 11, issue 10, 1-22

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.

Keywords: multiple sources; data quality; data repairing; entity resolution; truth discovery; data dependencies (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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