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Answering top-k queries over a mixture of attractive and repulsive dimensions
In this paper, we formulate a top- k query that compares objects in a database to a user-provided query object on a novel scoring function. The proposed scoring function combines the idea of attractive and repulsive dimensions into a general framework to overcome the weakness of traditional distance...
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Published in: | Proceedings of the VLDB Endowment 2011-11, Vol.5 (3), p.169-180 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | In this paper, we formulate a top-
k
query that compares objects in a database to a user-provided query object on a novel scoring function. The proposed scoring function combines the idea of
attractive
and
repulsive
dimensions into a general framework to overcome the weakness of traditional distance or similarity measures. We study the properties of the proposed class of scoring functions and develop efficient and scalable index structures that index the isolines of the function. We demonstrate various scenarios where the query finds application. Empirical evaluation demonstrates a performance gain of one to two orders of magnitude on querying time over existing state-of-the-art top-
k
techniques. Further, a qualitative analysis is performed on a real dataset to highlight the potential of the proposed query in discovering hidden data characteristics. |
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ISSN: | 2150-8097 2150-8097 |
DOI: | 10.14778/2078331.2078333 |