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Mathematical Problems in Engineering
Volume 2015, Article ID 236084, 12 pages
Research Article

Efficient ELM-Based Two Stages Query Processing Optimization for Big Data

1School of Information, Liaoning University, Shenyang, Liaoning 110036, China
2College of Information Science & Engineering, Northeastern University, Shenyang, Liaoning 110819, China

Received 22 August 2014; Accepted 27 November 2014

Academic Editor: Yi Jin

Copyright © 2015 Linlin Ding et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


MapReduce and its variants have emerged as viable competitors for big data analysis with a commodity cluster of machines. As an extension of MapReduce, ComMapReduce realizes the lightweight communication mechanisms to enhance the performance of query processing applications for big data. However, different communication strategies of ComMapReduce can substantially affect the executions of query processing applications. Although there is already the research work that can identify the communication strategies of ComMapReduce according to the characteristics of the query processing applications, some drawbacks still exist, such as relative simple model, too much user participation, and relative simple query processing execution. Therefore, an efficient ELM-based two stages query processing optimization model is proposed in this paper, named ELM to ELM (E2E) model. Then, we develop an efficient sample training strategy to train our E2E model. Furthermore, two query processing executions based on the E2E model, respectively, Just-in-Time execution and Queue execution, are presented. Finally, extensive experiments are conducted to verify the effectiveness and efficiency of the E2E model.