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http://dx.doi.org/10.5392/JKCA.2020.20.10.715

k-NN Query Optimization Scheme Based on Machine Learning Using a DNN Model  

We, Ji-Won (충북대학교 정보통신공학부)
Choi, Do-Jin (충북대학교 정보통신공학부)
Lee, Hyeon-Byeong (충북대학교 정보통신공학부)
Lim, Jong-Tae (충북대학교 정보통신공학부)
Lim, Hun-Jin (충북대학교 정보통신공학부)
Bok, Kyoung-Soo (원광대학교 SW융합학과)
Yoo, Jae-Soo (충북대학교 정보통신공학부)
Publication Information
The Journal of the Korea Contents Association / v.20, no.10, 2020 , pp. 715-725 More about this Journal
Abstract
In this paper, we propose an optimization scheme for a k-Nearest Neighbor(k-NN) query, which finds k objects closest to the query in the high dimensional feature vectors. The k-NN query is converted and processed into a range query based on the range that is likely to contain k data. In this paper, we propose an optimization scheme using DNN model to derive an optimal range that can reduce processing cost and accelerate search speed. The entire system of the proposed scheme is composed of online and offline modules. In the online module, a query is actually processed when it is issued from a client. In the offline module, an optimal range is derived for the query by using the DNN model and is delivered to the online module. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.
Keywords
Machine learning; K-Recent Query Processing; DNN Model; Distributed Processing; Spark;
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