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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Extra info for An Introduction to Duplicate Detection
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.