Abstract
As one variant of MapReduce framework, ComMapReduce adds the lightweight communication mechanisms to improve the performance of query processing programs. Although the existing research work has already solved the problem of how to identify the communication strategy of ComMapReduce, there are still some drawbacks, such as relative simple model and too much user participation. Therefore, in this paper, we propose a two stages query processing optimization model based on ELM, named ELM to ELM (E2E) model. Then, we develop efficient sample training strategy, predicting and execution algorithm to construct the E2E model. Finally, extensive experiments are conducted to verify the effectiveness and efficiency of the E2E model.
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References
Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Communications of the ACM 51(1), 107–113 (2008)
Ding, L., Xin, J., Wang, G., Huang, S.: ComMapReduce: An improvement of mapReduce with lightweight communication mechanisms. In: Lee, S.-g., Peng, Z., Zhou, X., Moon, Y.-S., Unland, R., Yoo, J. (eds.) DASFAA 2012, Part II. LNCS, vol. 7239, pp. 150–168. Springer, Heidelberg (2012)
Ding, L., Wang, G., Xin, J., Wang, X., Huang, S., Zhang, R.: Commapreduce: an improvement of mapreduce with lightweight communication mechanisms. Data Knowledge Engineering 88, 224–247 (2013)
Deng, D., Li, G., Hao, S., Wang, J., Feng, J.: Massjoin: A mapreduce-based method for scalable string similarity joins. In: ICDE, pp. 340–351. IEEE (2014)
Qin, L., Yu, J.X., Chang, L., Cheng, H., Zhang, C., Lin, X.: Scalable big graph processing in mapreduce. In: SIGMOD Conference, pp. 827–838. ACM (2014)
Ding, L., Xin, J., Wang, G.: An efficient query processing optimization based on elm in the cloud. Neural Computing and Applications (2014)
Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of the 2004 IEEE International Joint Conference on Neural Networks, vol. 2, pp. 985–990. IEEE (2004)
Lendasse, A., He, Q., Miche, Y., Huang, G.-B.: Advances in extreme learning machines (elm2012). Neurocomputing 128, 1–3 (2014)
Zong, W., Huang, G.-B.: Learning to rank with extreme learning machine. Neural Processing Letters 39(2), 155–166 (2014)
Basu, A., Shuo, S., Zhou, H., Lim, M.-H., Huang, G.-B.: Silicon spiking neurons for hardware implementation of extreme learning machines. Neurocomputing 102, 125–134 (2013)
Xi-Zhao, W., Qing-Yan, S., Qing, M., Jun-Hai, Z.: Architecture selection for networks trained with extreme learning machine using localized generalization error model. Neurocomputing 102, 3–9 (2013)
Zong, W., Huang, G.-B., Chen, Y.: Weighted extreme learning machine for imbalance learning. Neurocomputing 101, 229–242 (2013)
He, Q., Shang, T., Zhuang, F., Shi, Z.: Parallel extreme learning machine for regression based on mapreduce. Neurocomputing 102, 52–58 (2013)
Zhang, R., Lan, Y., Huang, G.-B., Xu, Z.-B., Soh, Y.C.: Dynamic extreme learning machine and its approximation capability. IEEE T. Cybernetics 43(6), 2054–2065 (2013)
Zhai, J.-H., Xu, H.-Y., Wang, X.-Z.: Dynamic ensemble extreme learning machine based on sample entropy. Soft Computing 16(9), 1493–1502 (2012)
Sun, Y., Yuan, Y., Wang, G.: An os-elm based distributed ensemble classification framework in p2p networks. Neurocomputing 74(16), 2438–2443 (2011)
Zhao, X.-g., Wang, G., Bi, X., Gong, P., Zhao, Y.: Xml document classification based on elm. Neurocomputing 74(16), 2444–2451 (2011)
Jun, W., Shitong, W., Chung, F.-l.: Positive and negative fuzzy rule system, extreme learning machine and image classification. International Journal of Machine Learning and Cybernetics 2(4), 261–271 (2011)
Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing 70(1), 489–501 (2006)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1226–1238 (2005)
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Ding, L., Liu, Y., Song, B., Xin, J. (2015). Two Stages Query Processing Optimization Based on ELM in the Cloud. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, KA. (eds) Proceedings of ELM-2014 Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-319-14063-6_9
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DOI: https://doi.org/10.1007/978-3-319-14063-6_9
Publisher Name: Springer, Cham
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