REBD:A Conceptual Framework for Big Data Requirements Engineering

TL;DR

This paper introduces REBD, a conceptual framework tailored for requirements engineering in big data projects, addressing the limitations of traditional methods to improve project success and productivity.

Contribution

The paper proposes REBD, a new data-centric requirements engineering framework specifically designed for big data projects, enhancing traditional methodologies.

Findings

Provides a systematic plan for big data project requirements engineering

Addresses limitations of traditional RE methods in big data context

Aims to improve project success and productivity

Abstract

Requirements engineering (RE), as a part of the project development life cycle, has increasingly been recognized as the key to ensuring on-time, on-budget, and goal-based delivery of software projects;compromising this vital phase is nothing but project failures. RE of big data projects is even more crucial because of the main characteristics of big data, including high volume, velocity, and variety. As the traditional RE methods and tools are user-centric rather than data-centric, employing these methodologies is insufficient to fulfill the RE processes for big data projects. Because of the importance of RE and limitations of traditional RE methodologies in the context of big data software projects, in this paper, a big data requirements engineering framework, named REBD, has been proposed. This conceptual framework describes the systematic plan to carry out big data projects starting from requirements engineering to the development, assuring successful execution, and increased productivity of the big data projects.