Multi-Source Data Repairing: A Comprehensive Survey

Autor:	Chen Ye, Haoyang Duan, Hengtong Zhang, Hua Zhang, Hongzhi Wang, Guojun Dai
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	multiple sources data quality data repairing entity resolution truth discovery data dependencies Mathematics QA1-939
Zdroj:	Mathematics, Vol 11, Iss 10, p 2314 (2023)
Druh dokumentu:	article
ISSN:	11102314 2227-7390
DOI:	10.3390/math11102314
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/363a35977afa4f51a469c24afdbbd30e Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.