By Felix Naumann, Melanie Herschel, M. Tamer Ozsu
With the ever expanding quantity of information, facts caliber difficulties abound. a number of, but diverse representations of an identical real-world gadgets in info, duplicates, are the most interesting facts caliber difficulties. the consequences of such duplicates are dangerous; for example, financial institution buyers can receive reproduction identities, stock degrees are monitored incorrectly, catalogs are mailed a number of occasions to an analogous family, and so on. immediately detecting duplicates is tough: First, reproduction representations aren't exact yet a little bit range of their values. moment, in precept all pairs of files may be in comparison, that's 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 establish 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. eventually, we speak about how you can overview the luck of replica detection. desk of Contents: information detoxing: advent and Motivation / challenge Definition / Similarity features / replica Detection Algorithms / comparing Detection luck / end and Outlook / Bibliography
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Additional info for An Introduction to Duplicate Detection
Clearly, there are no permutations of characters 34 3. SIMILARITY FUNCTIONS in σ so t = 0. 78. 1, we discussed token-based similarity measures that divide the data used for comparisons into sets of tokens, which are then compared based on equal tokens. We further discussed similarity measures that keep data as a whole in the form of a string and that compute the similarity of strings based on string edit operations that account for differences in the compared strings. In this section, we discuss similarity measures that combine both tokenization and string similarity in computing a final similarity score.
The Jaro similarity [Jaro, 1989] essentially compares two strings s1 and s2 by first identifying characters “common” to both strings. Two characters are said to be common to s1 and s2 if they are equal and if their positions within the two strings, denoted as i and j , respectively, do not differ by more than half of the length of the shorter string. 5 × min(|s1 |, |s2 |). Once all common characters have been identified, both s1 and s2 are traversed sequentially, and we determine the number t of transpositions of common characters where a transposition occurs when the i-th common character of s1 is not equal to the i-th common character of s2 .
Let us refer to the rules of equational theory as positive rules. , 2008]. 1. 13 Negative rule. ssn ⇒ c1 ≡ c2 This rule excludes candidates c1 and c2 being duplicates if their social security numbers do not match. We can combine positive and negative rules as a sequence of classifiers to form a duplicate profile, such that pairs not classified by classifier i are input to the subsequent classifier i + 1. It is interesting to note that the order of rules in a profile that mixes positive and negative rules affects the final output, in contrast to equational theory where the output is always the same no matter the order of the rules.
An Introduction to Duplicate Detection by Felix Naumann, Melanie Herschel, M. Tamer Ozsu