By Felix Naumann, Melanie Herschel, M. Tamer Ozsu
With the ever expanding quantity of information, info caliber difficulties abound. a number of, but assorted representations of an analogous real-world items in info, duplicates, are probably the most interesting info caliber difficulties. the results of such duplicates are harmful; for example, financial institution shoppers can receive reproduction identities, stock degrees are monitored incorrectly, catalogs are mailed a number of occasions to an identical family, and so forth. immediately detecting duplicates is hard: First, replica representations will not be exact yet a little bit vary of their values. moment, in precept all pairs of documents will be in comparison, that is infeasible for big 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 files. Well-chosen similarity measures enhance the effectiveness of replica detection. (ii) Algorithms are constructed to accomplish on very huge volumes of information in look for duplicates. Well-designed algorithms increase the potency of reproduction detection. ultimately, we speak about how you can assessment the luck 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 resources for An Introduction to Duplicate Detection
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.
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. In contrast to token-based measures, strings are considered as a whole and are not divided into sets of tokens. , insertion of characters, character swaps, deletion of characters, or replacement of characters.
We then discuss another technique, orthogonal to similarity measures, to classify pairs of candidates as duplicates or non-duplicates. 6). 24 3. 1 TOKEN-BASED SIMILARITY Token-based similarity measures compare two strings by first dividing them into a set of tokens using a tokenization function, which we denote as tokenize(·). Intuitively, tokens correspond to substrings of the original string. As a simple example, assume the tokenization function splits a string into tokens based on whitespace characters.