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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)


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.


Transactional database Artificial neural network Approximation query Pattern mining Data compression 



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


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.The University of TokyoTokyoJapan

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