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Journal of Intelligent Information Systems
Erschienen in: Journal of Intelligent Information Systems 1/2010

01.02.2010

Processing top-N relational queries by learning

verfasst von: Liang Zhu, Weiyi Meng, Chunnian Liu, Wenzhu Yang, Dazhong Liu

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 1/2010

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Abstract

A top-N selection query against a relation is to find the N tuples that satisfy the query condition the best but not necessarily completely. In this paper, we propose a new method for evaluating top-N queries against a relation. This method employs a learning-based strategy. Initially, this method finds and saves the optimal search spaces for a small number of random top-N queries. The learned knowledge is then used to evaluate new queries. Extensive experiments are carried out to measure the performance of this strategy and the results indicate that it is highly competitive with existing techniques for both low-dimensional and high-dimensional data. Furthermore, the knowledge base can be updated based on new user queries to reflect new query patterns so that frequently submitted queries can be processed most efficiently. The maintenance and stability of the knowledge base are also addressed in the paper.
Vorheriger Artikel A new classification of datasets for frequent itemsets
Nächster Artikel Using temporal constraints for temporal abstraction

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Fußnoten
1
In all the figures in Section 5.5, we arrange the legends and the corresponding curves in the same order to reduce confusion; the order of legends is from left to right (if any), and then from top to bottom.
 
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Metadaten
Titel
Processing top-N relational queries by learning
verfasst von
Liang Zhu
Weiyi Meng
Chunnian Liu
Wenzhu Yang
Dazhong Liu
Publikationsdatum
01.02.2010
Verlag
Springer US
Erschienen in
Journal of Intelligent Information Systems / Ausgabe 1/2010
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI
https://doi.org/10.1007/s10844-009-0078-7

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