Abstract

New generation databases also called NoSQL (Not only SQL) databases are highly scalable, flexible, and low-latent. These types of databases emerge as a result of the rigidity shown by traditional databases to handle today’s data which is voluminous, highly diversified and generated at a very high rate. With NoSQL, problems such as database expansion difficulties, low query performance and low storage capacity are addressed. However, the inherent complexity of contemporary datasets coupled with programmers’ low NoSQL modeling competence are increasingly making database modeling and design vastly challenging, especially when parameters like consistency, availability and scalability are to be balanced in accordance with system requirements. As such, a schema suggestion model for NoSQL databases is posed to address this balancing issue. The proposed model aims to abstractly suggest schemas at the initial stage of system development based on user defined system requirements and CRUD (Create, Read, Update and Delete) operations among others. This is achieved through the adaptation of exploratory and experimental approaches of research. Also, few mathematical formulas are introduced to calculate clusters availability during entity mappings. A comparison was conducted between the schema produced using the proposed model and the one without. Results obtained shows substantial improvement in the areas of security and read–write query performance.

Details

Title
Automatic schema suggestion model for NoSQL document-stores databases
Author
Abdullahi Abubakar Imam 1   VIAFID ORCID Logo  ; Shuib Basri 2 ; Rohiza Ahmad 2 ; Watada, Junzu 2 ; González-Aparicio, María T 3 

 Computer and Information Sciences Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Seri Iskandar, Perak, Malaysia; SQ2E Research Cluster, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Seri Iskandar, Perak, Malaysia; Ahmadu Bello University, Zaria, Nigeria 
 Computer and Information Sciences Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Seri Iskandar, Perak, Malaysia; SQ2E Research Cluster, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Seri Iskandar, Perak, Malaysia 
 Computing Department, University of Oviedo, Gijon, Spain 
Pages
1-17
Publication year
2018
Publication date
Dec 2018
Publisher
Springer Nature B.V.
e-ISSN
21961115
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2151027062
Copyright
Journal of Big Data is a copyright of Springer, (2018). All Rights Reserved., © 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.