A new catalog classifying 35 data error types into missing, incorrect, and redundant categories for tabular data, with definitions and examples to improve data quality management.
Information Systems133 (Aug
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RACT is a retrieval-augmented self-supervised method that improves multi-table schema matching precision and completeness by up to 70% by probabilistically retrieving relevant tables to limit column candidate search space.
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A Catalog of Data Errors
A new catalog classifying 35 data error types into missing, incorrect, and redundant categories for tabular data, with definitions and examples to improve data quality management.
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RACT: Retrieval Augmented Column-Table Learning and Prediction for Multi-Table Schema Matching
RACT is a retrieval-augmented self-supervised method that improves multi-table schema matching precision and completeness by up to 70% by probabilistically retrieving relevant tables to limit column candidate search space.