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Non-native Techniques for Storing JSON Documents into Relational Tables

Author:

Dušan PetkovićAuthors Info & Claims

iiWAS '20: Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services

Pages 16 - 20

https://doi.org/10.1145/3428757.3429103

Published: 27 January 2021 Publication History

Abstract

The natural way, how JSON documents can be queried and modified is to store them first in relational environment. In such a case, the features of relational DBMSs such as transaction processing, can be used. In this paper we compare two different mapping techniques: Adjacency List and the Single-Table Data Mapping (STDM) algorithm, which can be used, among other techniques, to store JSON documents in relational tables. The reason to choose and compare these two techniques is due to their origin: both are representatives of two different non-native storing techniques. The former is a general technique, which can be applied to any data presented in hierarchical form, while the latter is a representative of the family of XML-to-Relational storage algorithms, which can be used for JSON, too. Our results show that using the STDM algorithm the size of resulting relational table is approximately 70% of the size of the corresponding table generated with Adjacency List. Additionally, the STDM algorithm significantly outperforms Adjacency List concerning time.

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Cited By

Yuan GLu JYan ZWu S(2023)A Survey on Mapping Semi-Structured Data and Graph Data to Relational DataACM Computing Surveys10.1145/356744455:10(1-38)Online publication date: 2-Feb-2023
https://dl.acm.org/doi/10.1145/3567444
Zhang LPang KXu JNiu B(2022)JSON-based control model for SQL and NoSQL data conversion in hybrid cloud databaseJournal of Cloud Computing10.1186/s13677-022-00302-911:1Online publication date: 3-Aug-2022
https://doi.org/10.1186/s13677-022-00302-9

Index Terms

Non-native Techniques for Storing JSON Documents into Relational Tables
1. Information systems
  1. Data management systems
    1. Database management system engines

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cover image ACM Other conferences

iiWAS '20: Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services

November 2020

492 pages

ISBN:9781450389228

DOI:10.1145/3428757

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Johannes Kepler University, Linz, Austria

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 January 2021

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iiWAS '20

iiWAS '20: The 22nd International Conference on Information Integration and Web-based Applications & Services

November 30 - December 2, 2020

Chiang Mai, Thailand

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Cited By

Yuan GLu JYan ZWu S(2023)A Survey on Mapping Semi-Structured Data and Graph Data to Relational DataACM Computing Surveys10.1145/356744455:10(1-38)Online publication date: 2-Feb-2023
https://dl.acm.org/doi/10.1145/3567444
Zhang LPang KXu JNiu B(2022)JSON-based control model for SQL and NoSQL data conversion in hybrid cloud databaseJournal of Cloud Computing10.1186/s13677-022-00302-911:1Online publication date: 3-Aug-2022
https://doi.org/10.1186/s13677-022-00302-9

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