Abstract
Over the past years, the main objective of database management systems was to store data and give users the possibility to manipulate those data. SQL databases were sufficient in the first time, but with the evolution of computer science technologies, other factors start to appear; such as the volume of the stored data, the velocity of the transactions between the user and data, the possibility to store heterogeneous data (variety) and many other Big Data challenges. This paper presents the major differences between the SQL and NoSQL databases in term of variety, velocity and ease of programming. For SQL, we used the Oracle object-relational database and for NoSQL, we used MongoDB document-oriented database. The comparative study was applied to data modeled by graphs, where we consider a set of graphs and for each graph, we measure the time needed to insert the graph in the database, the size of the graph in the database and other factors. Measurements presents in this process are generally automatic and supported by a set of developed algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Sivasubramanian, S.: Amazon dynamoDB: a seamlessly scalable non-relational database service. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, SIGMOD 2012, pp. 729–730. ACM, New York (2012)
Chodorow, K.: MongoDB: The Definitive Guide: Powerful and Scalable Data Storage. O’Reilly Media, Newton (2013)
Parker, Z., Poe, S., Vrbsky, S.V.: Comparing NoSQL mongodb to an SQL db. ACMSE 2013, pp. 5–10 (2013)
Chang, F., et al.: Bigtable: a distributed storage system for structured data. ACM Trans. Comput. Syst. (TOCS) 26(2), 4 (2008)
Miller, J.J.: Graph database applications and concepts with Neo4j. In: Proceedings of the Southern Association for Information Systems Conference, Atlanta, GA, USA, vol. 2324, No. 36) (2013)
Vicknair, C., et al.: A comparison of a graph database and a relational database: a data provenance perspective. In: Proceedings of the 48th Annual Southeast Regional Conference, ACM, p. 42 (2010)
Ait El Mouden, Z., et al.: Towards for using spectral clustering in graph mining. big data, cloud and applications BDCA 2018. In: Communications in Computer and Information Science, CCIS, vol. 872, pp. 144–159, Springer (2018)
Ait El Mouden, Z., Jakimi, A., Hajar, M.: An application of spectral clustering approach to detect communities in data modeled by graphs. In: Proceedings of the 2nd International Conference on Networking, Information Systems & Security (NISS19), Article 4, ACM (2019)
Machkour, M., Afdel, K., Khamlichi, Y.I.: A reversible conversion methodology: between XML and object-relational models. in 2016 7th International Conference on Information and Communication Systems (ICICS), pp. 270–275 (2016)
Ait El Mouden, Z., Jakimi, A., Hajar, M.: An algorithm of conversion between relational data and graph schema. In: Proceeding of International Conference Europe Middle East & North Africa Information Systems and Technologies to Support Learning (EMENA-ISTL 2018), Smart Innovation Systems and Technologies, vol. 111, Springer International Publishing (2019)
Kamal, S.H., Elazhary, H.H., Hassanein, E.E.: A qualitative comparison of NoSQL data stores. Int. J. Adv. Comput. Sci. Appl. 10(2), 330–338 (2019)
Abbes, H., Gargouri, F.: MongoDB-based modular ontology building for big data integration. J. Data Semant. 7(1), 1–27 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ait El Mouden, Z., Jakimi, A., Hajar, M., Boutahar, M. (2020). Graph Schema Storage in SQL Object-Relational Database and NoSQL Document-Oriented Database: A Comparative Study. In: Serrhini, M., Silva, C., Aljahdali, S. (eds) Innovation in Information Systems and Technologies to Support Learning Research. EMENA-ISTL 2019. Learning and Analytics in Intelligent Systems, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-030-36778-7_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-36778-7_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36777-0
Online ISBN: 978-3-030-36778-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)