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

By Felix Naumann, Melanie Herschel, M. Tamer Ozsu

With the ever expanding quantity of information, info caliber difficulties abound. a number of, but varied representations of an analogous real-world gadgets in information, duplicates, are essentially the most exciting facts caliber difficulties. the consequences of such duplicates are hazardous; for example, financial institution shoppers can receive reproduction identities, stock degrees are monitored incorrectly, catalogs are mailed a number of instances to an identical family, and so on. immediately detecting duplicates is hard: First, reproduction representations will not be exact yet a bit vary of their values. moment, in precept all pairs of documents might be in comparison, that's infeasible for big volumes of information. This lecture examines heavily the 2 major elements to beat those problems: (i) Similarity measures are used to immediately determine duplicates whilst evaluating files. Well-chosen similarity measures increase the effectiveness of reproduction detection. (ii) Algorithms are built to accomplish on very huge volumes of information in look for duplicates. Well-designed algorithms enhance the potency of replica detection. ultimately, we talk about how you can assessment the good fortune of reproduction detection. desk of Contents: information detoxification: creation and Motivation / challenge Definition / Similarity capabilities / reproduction Detection Algorithms / comparing Detection good fortune / end and Outlook / Bibliography

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The same exercise can be repeated for the case where one string is equal to the other except for an additional suffix. , Peter J vs. Peter John stored as a first name). An extension of the Jaro similarity, called the Jaro-Winkler similarity [Winkler and Thiboudeau, 1991], considers this special case. Given two strings s1 and s2 with a common prefix ρ, the Jaro-Winkler similarity is computed as JaroWinklerSim(s1 , s2 ) = JaroSim(s1 , s2 ) + |ρ| × f × (1 − JaroSim(s1 , s2 )) where f is a constant scaling factor for how much the similarity is corrected upwards based on the common prefix ρ.

Hence, using the Smith-Waterman distance, the existence of a prefix or a suffix is less penalized than in the Levenshtein distance. The Smith-Waterman distance divides the original strings into a prefix, a common subexpression, and a suffix. , due to abbreviations, missing words, etc. , 1976]: It allows edit operations, notably insertion and deletion of complete blocks within a string and assigns these block insertions and deletions less weight than to the insertion or deletion of the individual characters in a block.

3 Generating q-grams. 2, which, as the following example illustrates results in a similarity score that is less sensitive to typographical errors than the previously described measures. 4 q-gram based token similarity computation. 3. We observe that the two token sets overlap in 13 q-grams, and we have a total of 22 distinct q-grams. 59. 342 × 4 where V and W are the q-gram sets of s1 and s2 , respectively. 30 3. 2 EDIT-BASED SIMILARITY Let us now focus on a second family of similarity measures, so called edit-based similarity measures.

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