REBD:A Conceptual Framework for Big Data Requirements Engineering
This work addresses the challenge of requirements engineering for big data software projects, which is crucial due to the high volume, velocity, and variety of data, but it appears incremental as it builds on existing RE concepts.
The authors tackled the problem of requirements engineering for big data projects, proposing a conceptual framework called REBD to address the limitations of traditional user-centric methods, aiming to ensure successful execution and increased productivity.
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.