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• W2789873537 abstract "[Context and Motivation] Big Data applications, like traditional applications, serve end-user needs except that underlying the software system is Big Data which the system operates upon. In comparison to traditional software development where the development processes are usually well-established, the processes for the development of applications involving Big Data are not clear just yet from the scientific literature – given the nature of computing involved and data characteristics such as volume, variety, veracity, and velocity. [Question/Problem] This, uncertain situation, has given rise to new questions, that is: “What are the early signs of the ways Big Data applications is treated in requirements engineering (RE)? What new directions in RE research are envisaged to promulgate further research?” [Principal ideas/Results] This paper presents the state of the art of requirements engineering (RE) research involving Big Data applications. Initially, 311 papers were identified from numerous sources from which, after methodical selection, 14 papers were deemed relevant for use in this study. Our investigation centres around: (i) phases of the RE process, (ii) type of requirements, (iii) application domains, (iv) RE research challenges, and (v) solution proposals targeted by RE research. Our key observation is that there isn’t a significant amount of research addressing: (a) aspects of RE in the context of Big Data applications; and (b) RE methods, tools and processes for the development of Big Data applications. Thus, this situation provides opportunities for research in this new area of RE. [Contribution] This paper presents the state-of-the-art of RE research in the context of Big Data applications, an analysis of the current research, and directions for further research." @default.
• W2789873537 created "2018-03-29" @default.
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• W2789873537 date "2018-01-01" @default.
• W2789873537 modified "2023-10-18" @default.
• W2789873537 title "State of Requirements Engineering Research in the Context of Big Data Applications" @default.
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• W2789873537 doi "https://doi.org/10.1007/978-3-319-77243-1_20" @default.
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