Database selection for processing k nearest neighbors queries in distributed environments

Yu, Clement; Sharma, Prasoon; Meng, Weiyi; Yan, Qin

doi:10.1145/379437.379504

Cited by 19 publications

(23 citation statements)

References 9 publications

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“…Zhu et al (2008) proposed region-clustering methods to evaluate concurrently a set of multiple top-N queries submitted (almost) at the same time. Yu et al (2001; introduced the methods for processing top-N queries in multiple database environments. The techniques are based on ranking databases.…”

Section: Related Workmentioning

confidence: 99%

“…We use S (Q, r, N) to denote this smallest possible n-square and the corresponding r is called the optimal search distance for the top-N query Q. Some example distance functions are (Bruno et al, 2002;Chen and Ling, 2002;Yu et al, 2001):…”

Section: Problem Definition and Analysismentioning

confidence: 99%

“…Different distance functions may lead to different sets of the top-N tuples. Most existing work on top-N query research proposes to let the user specify the distance (or scoring) function as part of the top-N query (Chaudhuri and Gravano, 1999;Yu et al, 2001).…”

Section: Problem Definition and Analysismentioning

confidence: 99%

“…For example, when buying a used car, a 100-dollar difference in price is probably more significant than a 100-mile difference in mileage. This can be resolved by multiplying appropriate importance/scaling factors to the raw distances based on the values of different attributes (Chen and Meng, 2003;Yu et al, 2001). These importance/scaling factors are specified by the user as part of the top-N query specification.…”

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confidence: 99%

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Processing top-N relational queries by learning

et al. 2009

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Problem Definition and Analysismentioning

confidence: 99%

Section: Problem Definition and Analysismentioning

confidence: 99%

mentioning

confidence: 99%

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Processing top-N relational queries by learning

et al. 2009

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“…Yu et al [41,42] introduced the methods for processing top-N queries in multiple database environments. The techniques are based on ranking databases.…”

Section: Related Workmentioning

confidence: 99%

Region clustering based evaluation of multiple top-N selection queries

Zhu

Meng

Yang

et al. 2008

Data & Knowledge Engineering

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In many database applications, there are opportunities for multiple top-N queries to be evaluated at the same time. Often it is more cost effective to evaluate multiple such queries collectively than individually.In this paper, we propose a new method for evaluating multiple top-N queries concurrently over a relational database. The basic idea of this method is region clustering that groups the search regions of individual top-N queries into larger regions and retrieves the tuples from the larger regions. This method avoids having the same region accessed multiple times and reduces the number of random I/O accesses to the underlying databases. Extensive experiments are carried out to measure the performance of this new strategy and the results indicate that it is significantly better than the naïve method of evaluating these queries one by one for both low-dimensional (2, 3, and 4) and high-dimensional (25, 50, and 104) data.

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P2P-Based Web Text Information Retrieval

Chen

Shi

2005

Web Technologies Research and Development - APWeb 2005

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Database selection for processing k nearest neighbors queries in distributed environments

Cited by 19 publications

References 9 publications

Processing top-N relational queries by learning

Processing top-N relational queries by learning

Region clustering based evaluation of multiple top-N selection queries

P2P-Based Web Text Information Retrieval

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