Felix Naumann, Melanie Herschel, M. Tamer Ozsu's An Introduction to Duplicate Detection PDF
By Felix Naumann, Melanie Herschel, M. Tamer Ozsu
With the ever expanding quantity of information, facts caliber difficulties abound. a number of, but varied representations of an analogous real-world gadgets in info, duplicates, are probably the most fascinating info caliber difficulties. the consequences of such duplicates are unsafe; for example, financial institution buyers can receive replica identities, stock degrees are monitored incorrectly, catalogs are mailed a number of instances to an identical loved ones, and so on. instantly detecting duplicates is hard: First, reproduction representations aren't exact yet a bit of vary of their values. moment, in precept all pairs of files can be in comparison, that is infeasible for giant volumes of information. This lecture examines heavily the 2 major parts to beat those problems: (i) Similarity measures are used to immediately determine duplicates while evaluating documents. Well-chosen similarity measures enhance the effectiveness of reproduction detection. (ii) Algorithms are built to accomplish on very huge volumes of knowledge in look for duplicates. Well-designed algorithms enhance the potency of replica detection. ultimately, we talk about ways to assessment the luck of replica detection. desk of Contents: information detoxing: advent and Motivation / challenge Definition / Similarity services / replica Detection Algorithms / comparing Detection good fortune / end and Outlook / Bibliography
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Additional info for An Introduction to Duplicate Detection
Assuming that a token t appears in the value v of an object description of a candidate c such that (a, v) ∈ OD(c), we denote its term frequency as tft,c . The intuition behind the inverse document frequency is that it assigns higher weights to tokens that occur less frequently in the scope of all candidate descriptions. , in a database listing insurance companies, the token Insurance is likely to occur very frequently across object descriptions and the idf thus assigns it a lower weight than to more distinguishing tokens such as Liberty or Prudential.
Essentially, the d dimensions of these vectors correspond to all d distinct tokens that appear in any string in a given finite domain, denoted as D. In our scenario, we assume that s1 and s2 originate from the same relational attribute, say a. In this case, D corresponds to all distinct tokens in all values of a. For large databases, this number of tokens may be large, so the vectors V and W have high dimensionality d in practice. At this point of the discussion, we know how large the vectors V and W are.
2. 4: Matching characters between Prof. John Doe and Dr. 4. We see that none of the matching lines crosses another matching line, which indicates that none of the common characters yields to a transposition, so we have t = 0. 9 The Jaro similarity generally performs well for strings with slight spelling variations. However, due to the restriction that common characters have to occur in a certain distance from each other, the Jaro distance does not cope well with longer strings separating common characters.
An Introduction to Duplicate Detection by Felix Naumann, Melanie Herschel, M. Tamer Ozsu