Adaptive detection of approximately duplicate database records and the database integration approach to information discovery
Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem
Data Mining and Knowledge Discovery
Data Quality: The Accuracy Dimension
Data Quality: The Accuracy Dimension
Adaptive duplicate detection using learnable string similarity measures
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming and Delivering Data
Eliminating fuzzy duplicates in data warehouses
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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The data quality assessment process consists of several phases; the first phase is the data profiling step. The result of this step is the set of the most current metadata describing the examined data set. We present here a method for automatic discovery of reference data for textual attributes. Our method combines the textual similarity approach with the characteristics of attribute value distribution. The method can discover the correct reference data values also in situations where there is a large number of data impurities. The results of the experiments performed on real address data prove that the method can effectively discover the current reference data.