| Autor: |
Gurský, Peter, Vojtáš, Peter |
| Zdroj: |
Scalable Uncertainty Management (9783540879923); 2008, p243-255, 13p |
| Abstrakt: |
Methods of top-k search with no random access can be used to find k best objects using sorted access to the sources of attribute values. In this paper we present new heuristics over the NRA algorithm that can be used for fast search of top-k objects using wide range of user preferences. NRA algorithm usually needs a periodical scan of a large number of candidates during the computation. In this paper we propose methods of no random access top-k search that optimize the candidate list maintenance during the computation to speed up the search. The proposed methods are compared to a table scan method typically used in databases. We present results of experiments showing speed improvement depending on number of object attributes expressed in a user preferences or selectivity of user preferences. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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