EAN: 9783031007071

Bilder-Quelle: discount24.de - Sport-Freizeit

With the ever increasing volume of data data quality problems abound. Multiple yet different representations of the same real-world objects in data duplicates are one of the most intriguing data quality problems. The effects of such duplicates are detrimental for instance bank customers can obtain duplicate identities inventory levels are monitored incorrectly catalogs are mailed multiple times to the same household etc. Automatically detecting duplicates is difficult: First duplicate representations are usually not identical but slightly differ in their values. Second in principle all pairs of records should be compared which is infeasible for large volumes of data. This lecture examines closely the two main components to overcome these difficulties: (i) Similarity measures are used to automatically identify duplicates when comparing two records. Well-chosen similarity measures improve the effectiveness of duplicate detection. (ii) Algorithms are developed to perform on very large volumes of data in search for duplicates. Well-designed algorithms improve the efficiency of duplicate detection. Finally we discuss methods to evaluate the success of duplicate detection. Table of Contents: Data Cleansing: Introduction and Motivation Problem Definition Similarity Functions Duplicate Detection Algorithms Evaluating Detection Success Conclusion and Outlook Bibliography

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28.03.2025 um 21:19 Uhr

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