Download An Introduction to Duplicate Detection by Felix Naumann, Melanie Herschel, M. Tamer Ozsu PDF

By Felix Naumann, Melanie Herschel, M. Tamer Ozsu

With the ever expanding quantity of knowledge, facts caliber difficulties abound. a number of, but various representations of an analogous real-world gadgets in facts, duplicates, are probably the most exciting information caliber difficulties. the results of such duplicates are damaging; for example, financial institution consumers can receive replica identities, stock degrees are monitored incorrectly, catalogs are mailed a number of occasions to an analogous family, and so on. immediately detecting duplicates is hard: First, replica representations are not exact yet a little vary of their values. moment, in precept all pairs of documents might be in comparison, that's infeasible for giant volumes of information. This lecture examines heavily the 2 major elements to beat those problems: (i) Similarity measures are used to instantly determine duplicates whilst evaluating files. Well-chosen similarity measures enhance the effectiveness of replica detection. (ii) Algorithms are built to accomplish on very huge volumes of information in look for duplicates. Well-designed algorithms enhance the potency of reproduction detection. ultimately, we speak about easy methods to review the luck of replica detection. desk of Contents: facts detoxification: advent and Motivation / challenge Definition / Similarity services / replica Detection Algorithms / comparing Detection good fortune / end and Outlook / Bibliography

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According to this matrix, LevDist(Sean, Shawn) = 2. As mentioned at the beginning of this section, the Levenshtein distance is a special case of an edit distance as it uses unit weight and three basic edit operators (insert, delete, and replace character). , when one string is a prefix of the second string (Prof. John Doe vs. John Doe) or when strings use abbreviations (Peter J Miller vs. Peter John Miller). These problems are primarily due to the fact that all edit operations have equal weight and that each character is considered individually.

The third property, the element cardinality adds complexity to similarity measurement when we consider duplicate detection in relational data with relationships between candidate types. In- 22 2. PROBLEM DEFINITION deed, it is true that zero or more candidates of the same type may occur in the relationship description of a given candidate. When computing the similarity of two candidates based on their relationship description, different possibilities to align the candidates in the relationship descriptions exist.

However, in different domains, the difference in numbers has different meanings. For instance, when measuring differences on a microscopic scale, a difference of 1 mm is a large difference, whereas on a macroscopic scale 1 mm is almost nothing. A possible way to “normalize” such a difference is to take the distribution of values in the domain into account. Structural similarity. As a final remark, we point out that none of the similarity measures discussed so far considers the structure of the data; they all focus on content.

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