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Indexing Metric Spaces

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Distance indexing

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Metric space indexing is closely related to the recent digitization revolution where almost everything that one can see, hear, read, write or measure is available in digital form. Unlike traditional attribute-like data types such as numbers and strings of sortable domains, instances of these new data types are complex, and the only measure of comparison to apply is a sort of similarity. Such a situation implies an application of the query-by-example search paradigm where the database is searched for objects that are near the example object, also called the query object. A useful abstraction of this similarity is to see it as mathematical metric space [7]. The problem of organizing and searching large datasets of complex objects can then be considered from the perspective of generic or arbitrary metric spaces, sometimes labeled distance spaces. In general, the search problem can be described as follows:

Let D be a domain, d a distance measure on D, and...

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Recommended Reading

  1. Batko M, Novak D, Falchi F, Zezula P. On scalability of the similarity search in the world of peers. In: Proceedings of the 1st International Conference Scalable Information Systems; 2006. p. 1–12.

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  4. Dohnal V, Gennaro C, Savino P, Zezula P. D-Index: distance searching index for metric data sets. Multimedia Tools Appl. 2003;21(1):9–33.

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  10. Zezula P, Amato G, Dohnal V, Batko M. Similarity search: the metric space approach. Berlin: Springer; 2006.

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Correspondence to Pavel Zezula .

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Zezula, P., Batko, M., Dohnal, V. (2018). Indexing Metric Spaces. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_199

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