By Felix Naumann, Melanie Herschel, M. Tamer Ozsu
With the ever expanding quantity of information, facts caliber difficulties abound. a number of, but diversified representations of a similar real-world gadgets in facts, duplicates, are some of the most interesting information caliber difficulties. the consequences of such duplicates are unsafe; for example, financial institution shoppers can receive replica identities, stock degrees are monitored incorrectly, catalogs are mailed a number of instances to an analogous loved ones, and so forth. immediately detecting duplicates is hard: First, replica representations will not be exact yet somewhat fluctuate of their values. moment, in precept all pairs of files will be in comparison, that is infeasible for big volumes of knowledge. This lecture examines heavily the 2 major parts to beat those problems: (i) Similarity measures are used to immediately determine duplicates whilst evaluating documents. Well-chosen similarity measures enhance the effectiveness of reproduction detection. (ii) Algorithms are constructed 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 how you can overview the good fortune of replica detection. desk of Contents: facts detoxification: creation and Motivation / challenge Definition / Similarity capabilities / replica Detection Algorithms / comparing Detection good fortune / end and Outlook / Bibliography
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Additional resources for An Introduction to Duplicate Detection
9) Computation proceeds from the top left of the matrix to the bottom right. Once all values in the matrix have been computed, the result of the algorithm that corresponds to the Levenshtein distance can be retrieved from M|s1 |,|s2 | . 6 Computing the Levenshtein distance using dynamic programming. Consider again the two strings s1 = Sean and s2 = Shawn. 2. 3: Computing the edit distance using a dynamic programming approach initialized the matrix and computed the values of the first row and first column.
Ssn 123 345 678 fname Peter Jane John mname John Jack (a) PersDB1 lname Miller Smith Doe age 46 33 9 ssn 123 345 679 name Peter Miller Jane B. name) ⇒ c1 ≡ c2 We observe that terms may use complex comparison functions: for instance, in the first rule, we first concatenate the three name components of c1 and then compare the concatenated result to the name of c2 , using similarity (≈) as comparison operator. This operator indicates that the two strings being compared need to be similar, which is, for instance, determined using one of the previously defined similarity measures.
The Levenshtein distance of these two strings is 2, as we need to (i) replace the e in s1 by an h and (ii) insert a w to s1 to transform s1 into s2 . , we may delete all characters of s1 and subsequently insert all characters of s2 . However, the number of edit operations in this case would be 9, which is not minimal. 5 Levenshtein distance. A popular algorithm to compute the Levenshtein distance is based on dynamic programming. It starts by initializing a (|s1 | + 1) × (|s2 | + 1) matrix M, where |s| denotes the length of a string s.
An Introduction to Duplicate Detection by Felix Naumann, Melanie Herschel, M. Tamer Ozsu