By Felix Naumann, Melanie Herschel, M. Tamer Ozsu
With the ever expanding quantity of information, info caliber difficulties abound. a number of, but diverse representations of an analogous real-world gadgets in facts, duplicates, are some of the most interesting facts caliber difficulties. the results of such duplicates are dangerous; for example, financial institution consumers can receive reproduction identities, stock degrees are monitored incorrectly, catalogs are mailed a number of instances to an identical loved ones, and so on. immediately detecting duplicates is tough: First, replica representations should not exact yet somewhat range of their values. moment, in precept all pairs of documents may be in comparison, that is infeasible for giant volumes of information. This lecture examines heavily the 2 major elements to beat those problems: (i) Similarity measures are used to immediately establish duplicates whilst evaluating documents. 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 tips on how to overview the luck of reproduction detection. desk of Contents: facts detoxification: advent and Motivation / challenge Definition / Similarity features / replica Detection Algorithms / comparing Detection good fortune / end and Outlook / Bibliography
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Additional info for An Introduction to Duplicate Detection
Due to its high computational complexity, it is rarely used for duplicate detection. 6 RULE-BASED RECORD COMPARISON Up to this point, we have discussed similarity measures and distance measures that compute a realvalued score. This score is then input to a duplicate classifier as described at the beginning of this chapter (see p. 23). As a reminder, if the similarity score, as returned by a similarity measure, is above a given threshold θ, the compared pair of candidates is classified as a duplicate and as a non-duplicate, otherwise.
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. 12. Then, the pair of Persons with respective ssn of 678 and 679 would be classified as non-duplicates and they would not be passed on to the classifier using Rule 3, so they are not classified as duplicates. Both positive rules and negative rules only use information that is present in both candidates.
An aggregation function A then aggregates the individual weight. Using the two extensions above, the hybrid Jaccard similarity is defined as HybridJaccard = A(ti ,tj )∈Shared(s1 ,s2 ) w(ti , tj ) A(ti ,tj )∈Shared(s1 ,s2 ) w(ti , tj ) + A(ti )∈Unique(s1 ) w(ti ) + A(tj )∈Unique(s2 ) w(tj ) Note that instead of a secondary string similarity measure, we may also use a string distance measure to identify similar tokens. In this case, we simply replace TokenSim(ti , tj ) > θstring by TokenDist(ti , tj ) ≤ θstring .