Enhancing the management of unstructured data in e-learning systems using MongoDB

Abstract

Purpose – Current e-learning platforms are based on relational database management systems (RDBMS) and are well suited for handling structured data. However, it is expected from e-learning solutions to efficiently handle unstructured data as well. The purpose of this paper is to show an alternative to current solutions for unstructured data management. Design/methodology/approach – Current repository-based solution for file management was compared to MongoDB architecture according to their functionalities and characteristics. This included several categories: data integrity, hardware acquisition, processing files, availability, handling concurrent users, partition tolerance, disaster recovery, backup policies and scalability. Findings – This paper shows that it is possible to improve e-learning platform capabilities by implementing a hybrid database architecture that incorporates RDBMS for handling structured data and MongoDB database system for handling unstructured data. Research limitations/implications – The study shows an acceptable adoption of MongoDB inside a service-oriented architecture (SOA) for enhancing e-learning solutions. Practical implications – This research enables an efficient file handling not only for e-learning systems, but also for any system where file handling is needed. Originality/value – It is expected that future single/joint e-learning initiatives will need to manage huge amount of files and they will require effective file handling solution. The new architecture solution for file handling is offered in this paper: it is different from current solutions because it is less expensive, more efficient, more flexible and requires less administrative and development effort for building and maintaining.