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 knowledge, facts caliber difficulties abound. a number of, but assorted representations of an identical real-world items in facts, duplicates, are some of the most exciting information caliber difficulties. the results of such duplicates are harmful; for example, financial institution clients can receive replica identities, stock degrees are monitored incorrectly, catalogs are mailed a number of occasions to a similar family, and so on. immediately detecting duplicates is hard: First, replica representations aren't exact yet somewhat range of their values. moment, in precept all pairs of documents might be in comparison, that is infeasible for big volumes of knowledge. This lecture examines heavily the 2 major parts to beat those problems: (i) Similarity measures are used to instantly establish duplicates while evaluating files. Well-chosen similarity measures increase the effectiveness of replica detection. (ii) Algorithms are built to accomplish on very huge volumes of knowledge in look for duplicates. Well-designed algorithms increase the potency of replica detection. ultimately, we talk about tips on how to evaluation the good fortune of replica detection. desk of Contents: info detoxification: advent and Motivation / challenge Definition / Similarity services / reproduction Detection Algorithms / comparing Detection good fortune / end and Outlook / Bibliography

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DATA IN COMPLEX RELATIONSHIPS 21 Element context. An element may appear in various contexts within an XML hierarchy, or, in general, in a graph of relationships. The context of an XML element is given by its nesting in the XML hierarchy, for instance, identified by a path expression from the root element. The schema essentially specifies where an XML element can occur. As an example, the author element may be a child of book, in case the book has only one author. Otherwise, an author appears in a sequence of authors and has the path book/authors/author.

Probabilities are propagated from the leaves of the Bayesian network (that correspond to probabilities of pairs of simple elements) to the root and can be interpreted as similarities. As nodes either represent pairs or sets of elements, the different semantics of a missing element vs. a NULL value cannot be captured because the lack of an element results in the probability node not being created at all. 2 that arise when devising a similarity measure for XML data. However, DogmatiX does not distinguish between the different semantics that both element optionality and element context allow.

In the relational case, the structure of data is given, and the only means of specifying that a value is not further specified is the use of a NULL value. This choice exists in XML in addition to element optionality. 3. DATA IN COMPLEX RELATIONSHIPS 21 Element context. An element may appear in various contexts within an XML hierarchy, or, in general, in a graph of relationships. The context of an XML element is given by its nesting in the XML hierarchy, for instance, identified by a path expression from the root element.

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