NoSQL Database Modeling and Management: A Systematic Literature Review

Author:

Aguilar Vera Raul,Naal Jácome Andrés,Díaz Mendoza Julio,Gómez Gómez Omar

Abstract

The NoSQL databases that emerged this century were created to solve the limitations of relational database systems due to the different types of data that have appeared for information processing. In this paper, we present the results of a secondary study carried out to find and synthesize the research made up to now on modeling processes, characteristics of the used types of data, and management tools for NoSQL Databases. Currently, four types are recognized and classified according to the data model they use: key-value, document-oriented, column-based, and graph-based. With this study, it was possible to identify that the most frequently type of NoSQL database model is that of documents because it offers greater flexibility and versatility compared to the other three models. Although it offers more complex search methods, in terms of data, column and document schemas are the ones that usually describe their characteristics. It was also possible to observe a trend in the use of the column-oriented model and the document-oriented model in the management tools, and, although they all comply with the basic functionalities, the differences lie in the way in which the information is stored and the way they can be accessed.

Publisher

Universidad Pedagogica y Tecnologica de Colombia

Subject

Materials Science (miscellaneous)

Reference74 articles.

1. C. Coronel, S. Morris, P. Rob, Base de datos: diseño, implementación y administración, Cengage Learning Editores, 2011.

2. E. Codd, "A Relational Model of Data for Large Shared Data Banks", Communications of the ACM, vol. 13, no. 6, pp. 377-387, 1970. https://doi.org/10.1145/357980.358007

3. P.J. Sadalage, M. Fowler, NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, Addison-Wesley Professional, 2012.

4. P. Neubauer, NOSQL and Neo4j. https://www.scitepress.org/Papers/2017/63560/63560.pdf

5. R. Cattell, "Scalable SQL and NoSQL data stores," ACM Sigmod Record, vol. 39, no. 4, pp. 12-27, 2011. https://doi.org/10.1145/1978915.1978919

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3