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A Survey on Mapping Semi-Structured Data and Graph Data to Relational Data.

Authors :
GONGSHENG YUAN
JIAHENG LU
ZHENGTONG YAN
SAI WU
Source :
ACM Computing Surveys. Oct2023, Vol. 55 Issue 10, p1-38. 38p.
Publication Year :
2023

Abstract

The data produced by various services should be stored and managed in an appropriate format for gaining valuable knowledge conveniently. This leads to the emergence of various data models, including relational, semi-structured, and graph models, and so on. Considering the fact that the mature relational databases established on relational data models are still predominant in today’s market, it has fueled interest in storing and processing semi-structured data and graph data in relational databases so that mature and powerful relational databases’ capabilities can all be applied to these various data. In this survey, we review existing methods on mapping semi-structured data and graph data into relational tables, analyze their major features, and give a detailed classification of those methods. We also summarize the merits and demerits of each method, introduce open research challenges, and present future research directions. With this comprehensive investigation of existing methods and open problems, we hope this survey can motivate new mapping approaches through drawing lessons from each model’s mapping strategies, as well as a new research topic - mapping multi-model data into relational tables. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
03600300
Volume :
55
Issue :
10
Database :
Academic Search Index
Journal :
ACM Computing Surveys
Publication Type :
Academic Journal
Accession number :
161666016
Full Text :
https://doi.org/10.1145/3567444