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Memorizing Transactional Databases Compressively in Deep Neural Networks for Efficient Itemset Support Queries

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Book cover Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10635))

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Abstract

Can a deep neural network memorize a database? Though deep artificial neural networks are remarkable for large memory capacity that makes fitting any dataset possible, memorizing a database is a novel learning task unlike other popular tasks which intrinsically model mappings rather than “memorize” information internally. We give a positive answer to the question by showing that through training with maximal/minimal and frequent/infrequent patterns of a transactional database, a dynamically constructed deep net can support random itemset support queries with relatively high precision in regard to data compression ratio. Due to the compressive memorization, the amount of transactions in the database becomes irrelevant to the query time cost in our efficient method. We further discuss the potential interpretation of learnt database representation by analyzing corresponding statistical features of the database and activation patterns of the neural network.

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Notes

  1. 1.

    Zero-support (1-infrequent) itemsets are truncated due to excessive cardinality.

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Acknowledgments

This work was supported by JST CREST Grant Number JPMJCR1304, JSPS KAKENHI Grant Numbers JP16H01836, and JP16K12428.

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Authors and Affiliations

  1. The University of Tokyo, Hongo, Bunkyo, Tokyo, 113-8654, Japan

    Yi Ji & Yukio Ohsawa

Authors
  1. Yi Ji
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  2. Yukio Ohsawa
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Correspondence to Yi Ji .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Ji, Y., Ohsawa, Y. (2017). Memorizing Transactional Databases Compressively in Deep Neural Networks for Efficient Itemset Support Queries. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_54

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  • DOI: https://doi.org/10.1007/978-3-319-70096-0_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70095-3

  • Online ISBN: 978-3-319-70096-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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