EAN: 9783031007071

SYNTHESIS LECTURES ON DATA MANAGEMENT AN INTR...

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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