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Automatic Document Data Storage System Based on Machine Learning

  • Yu Yan
  • , Hongzhi Wang*
  • , Jian Zou
  • , Yixuan Wang
  • *Corresponding author for this work
  • Harbin Institute of Technology
  • Peng Cheng Laboratory

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Document storage management plays a significant role in the field of database. With the advent of big data, making storage management manually becomes more and more difficult and inefficient. There are many researchers to develop algorithms for automatic storage management(ASM). However, at present, no automatic systems or algorithms related to document data has been developed. In order to realize the ASM of document data, we firstly propose an automatic document data storage system (ADSML) based on machine learning, a user-friendly management system with high efficiency for achieving storage selection and index recommendation automatically. In this paper, we present the architecture and key techniques of ADSML, and describe three demo scenarios of our system.

Original languageEnglish
Title of host publicationWeb and Big Data - 4th International Joint Conference, APWeb-WAIM 2020, Proceedings
EditorsXin Wang, Rui Zhang, Young-Koo Lee, Le Sun, Yang-Sae Moon
PublisherSpringer Science and Business Media Deutschland GmbH
Pages551-555
Number of pages5
ISBN (Print)9783030602895
DOIs
StatePublished - 2020
Event4th Asia-Pacific Web and Web-Age Information Management, Joint Conference on Web and Big Data, APWeb-WAIM 2020 - Tianjin, China
Duration: 18 Sep 202020 Sep 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12318 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th Asia-Pacific Web and Web-Age Information Management, Joint Conference on Web and Big Data, APWeb-WAIM 2020
Country/TerritoryChina
CityTianjin
Period18/09/2020/09/20

Keywords

  • Automatic management
  • Index recommendation
  • Machine learning
  • Storage selection

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