Operators for Similarity Search: Semantics, Techniques and by Deepak P, Prasad M. Deshpande
By Deepak P, Prasad M. Deshpande
This booklet presents a finished educational on similarity operators. The authors systematically survey the set of similarity operators, essentially targeting their semantics, whereas additionally touching upon mechanisms for processing them effectively.
The publication starts via delivering introductory fabric on similarity seek platforms, highlighting the crucial position of similarity operators in such platforms. this can be by means of a scientific labeled review of the range of similarity operators which have been proposed in literature over the past 20 years, together with complex operators resembling RkNN, opposite k-Ranks, Skyline k-Groups and K-N-Match. considering that indexing is a middle know-how within the useful implementation of similarity operators, a variety of indexing mechanisms are summarized. eventually, present study demanding situations are defined, for you to let readers to spot strength instructions for destiny investigations.
In precis, this booklet bargains a accomplished assessment of the sector of similarity seek operators, permitting readers to appreciate the realm of similarity operators because it stands this day, and likewise delivering them with the historical past had to comprehend fresh novel approaches.
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Additional resources for Operators for Similarity Search: Semantics, Techniques and Usage Scenarios
28. -L. -K. Eng, and B. C. Ooi. Efficient progressive skyline computation. In VLDB, pages 301–310, 2001. 29. Y. Tao, X. Xiao, and J. Pei. Subsky: Efficient computation of skylines in subspaces. In ICDE, page 65, 2006. 30. A. K. Tung, R. Zhang, N. Koudas, and B. C. Ooi. Similarity search: a matching based approach. In Proceedings of the 32nd international conference on Very large data bases, pages 631–642. VLDB Endowment, 2006. 31. J. K. Uhlmann. Satisfying general proximity/similarity queries with metric trees.
In ICDE, page 65, 2006. 30. A. K. Tung, R. Zhang, N. Koudas, and B. C. Ooi. Similarity search: a matching based approach. In Proceedings of the 32nd international conference on Very large data bases, pages 631–642. VLDB Endowment, 2006. 31. J. K. Uhlmann. Satisfying general proximity/similarity queries with metric trees. Information Processing Letters, 40(4):175–179, 1991. 32. E. Vidal. New formulation and improvements of the nearest-neighbour approximating and eliminating search algorithm (aesa).
In both of these, the query is assumed to be at the origin, and the x and y co-ordinates of each object is determined according to the distance of the object from the query on the x and y attributes; in this toy example, we will assume that the schema contains just two attributes. , ) that exploit bounds in similarity values to bound the similarity of an object to the query, and can determine the membership in the top-k result set using such bounds; however, these would be unable to provide the actual score of each object in the result, and are hence not applicable in scenarios that need quantifying the weighted sum similarity exactly.