Proposing Field Matching Similarity Methods
Implementation and Comparison of Field Similarity Metrics with Duplicate Entities Detection Purpose in Database
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Duplicate records do not have a common key but refer to a unit entity. Databases that include these records have often some errors which cause the matching problem in duplicate records becomes a complex problem. These errors are: typing errors, incomplete information such as abbreviations, ignoring of standard formats or a combination of the above factors. In this book, databases are used in which typing errors are more than other errors. This database contains real estate information that includes 4 fields: name, surname, property address and property area. The goals of this book are: a revie...
Duplicate records do not have a common key but refer to a unit entity. Databases that include these records have often some errors which cause the matching problem in duplicate records becomes a complex problem. These errors are: typing errors, incomplete information such as abbreviations, ignoring of standard formats or a combination of the above factors. In this book, databases are used in which typing errors are more than other errors. This database contains real estate information that includes 4 fields: name, surname, property address and property area. The goals of this book are: a review on existing algorithms in identifying duplicate data in the fields which are: Edit-distance, Smith-waterman, Jaro, Jaro-Winkler, Lcs and N-gram; description of the proposed algorithms was presented to improve the efficiency and increase the precision of identifying duplication which are the proposed token-based algorithm and the proposed algorithm based on typing error; and comparing thesealgorithms efficiency in a large Persian database.
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