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- W3208310216 abstract "Variety of applications such as information extraction, data mining, e-learning, or web applications use heterogeneous and distributed data. As a result, the usage of data is challenged by deduplication issues. To harmonize this issue, the present study proposed a novel dedupe learning method (DLM) and other algorithms to detect and correct contextual data quality anomalies. The method was created and implemented on structured data. Our methods have been successful in identifying and correcting more data anomalies than current taxonomy techniques. Consequently, these proposed methods would be important in detecting and correcting errors in broad contextual data (big data)." @default.
- W3208310216 created "2021-11-08" @default.
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- W3208310216 date "2021-01-01" @default.
- W3208310216 modified "2023-09-26" @default.
- W3208310216 title "Implicit Dedupe Learning Method on Contextual Data Quality Problems" @default.
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- W3208310216 doi "https://doi.org/10.1007/978-3-030-71704-9_22" @default.
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