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International Conference of Pioneering Computer Scientists, Engineers and Educators

ICPCSEE 2020: Data Science pp 3–15Cite as

General Model for Index Recommendation Based on Convolutional Neural Network

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1257))

Abstract

With the advent of big data, the cost of index recommendation (IR) increases exponentially, and the portability of IR model becomes an urgent problem to be solved. In this paper, a fine-grained classification model based on multi-core convolution neural network (CNNIR) is proposed to implement the transferable IR model. Using the strong knowledge representation ability of convolution network, CNNIR achieves the effective knowledge representation from data and workload, which greatly improves the classification accuracy. In the test set, the accuracy of model classification reaches over \(95\%\). CNNIR has good robustness which can perform well under a series of different learning rate settings. Through experiments on MongoDB, the indexes recommended by CNNIR is effective and transferable.

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Acknowledgement

This paper was partially supported by NSFC grant U1866602, 61602129, 61772157.

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Authors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Yu Yan & Hongzhi Wang

  2. Peng Cheng Laboratory, Shenzhen, China

    Hongzhi Wang

Authors
  1. Yu Yan
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  2. Hongzhi Wang
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Corresponding author

Correspondence to Hongzhi Wang .

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Editors and Affiliations

  1. North University of China, Taiyuan, China

    Jianchao Zeng

  2. Northeast Forestry University, Harbin, China

    Weipeng Jing

  3. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  4. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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Yan, Y., Wang, H. (2020). General Model for Index Recommendation Based on Convolutional Neural Network. In: Zeng, J., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7981-3_1

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  • DOI: https://doi.org/10.1007/978-981-15-7981-3_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-7980-6

  • Online ISBN: 978-981-15-7981-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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