Download Operators for Similarity Search: Semantics, Techniques and by Deepak P, Prasad M. Deshpande PDF

By Deepak P, Prasad M. Deshpande

This publication offers a complete instructional on similarity operators. The authors systematically survey the set of similarity operators, basically concentrating on their semantics, whereas additionally touching upon mechanisms for processing them effectively.

The publication begins by means of delivering introductory fabric on similarity seek platforms, highlighting the vital function of similarity operators in such structures. this can be by means of a scientific categorised evaluate of the diversity of similarity operators which were proposed in literature during the last twenty years, together with complicated operators comparable to RkNN, opposite k-Ranks, Skyline k-Groups and K-N-Match. because indexing is a center know-how within the useful implementation of similarity operators, quite a few indexing mechanisms are summarized. eventually, present learn demanding situations are defined, with a purpose to allow readers to spot capability instructions for destiny investigations.

In precis, this ebook bargains a complete review of the sector of similarity seek operators, permitting readers to appreciate the world of similarity operators because it stands this day, and also delivering them with the historical past had to comprehend fresh novel approaches.

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Extra info for Operators for Similarity Search: Semantics, Techniques and Usage Scenarios

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Weikum. Io-top-k: Index-access optimized top-k query processing. In VLDB, pages 475–486, 2006. 3. J. L. Bentley. Multidimensional binary search trees used for associative searching. Commun. ACM, 18(9):509–517, 1975. 4. S. Borzsony, D. Kossmann, and K. Stocker. The skyline operator. In Data Engineering, 2001. Proceedings. 17th International Conference on, pages 421–430. IEEE, 2001. 5. -Y. Chan, H. -L. Tan, A. K. Tung, and Z. Zhang. On high dimensional skylines. In Advances in Database Technology-EDBT 2006, pages 478–495.

3) i=1 In the transformation for the skyline operator, where the sO (q, x) is simply the s(q, x) vector itself, all attributes are seen to be directly contributing since the similarities on each attribute are carried forward to the transformed representation. Without much ado, the classification under this criterion is as outlined below. All-Attribute Operator: An operator where similarities on all attributes directly contribute to the transformed operator-specific representation. Some-Attribute Operator: An operator where only the similarities on some attributes directly contribute to the transformed operator-specific representation.

Similarly, N-match with N set to the total number of attributes corresponds to the max, since it picks the attribute with the largest distance. 3 Filter Functions After condensing the (dis)similarity vectors by aggregation, they are fed into a filter step that chooses a subset of objects to be in the final result set RO (q, X ) based on these (dis)similarity scores. As with other steps, there is choice of various types of filters that can be used. Some of the common ones are described below. 1 Threshold Filter This is a simple filter that uses a threshold on each component of the condensed vector dO (q, x).

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