There are a few approaches that deal with the problematic of representing and obtaining goals; for example, the GORE
(Goal-Oriented Requirement Engineering), i*, or KAOS (Knowledge Acquisition in autOmated Specification). None of these
methodologies are specific for Big Data ecosystems; however, they can be used to achieve this purpose.
Furthermore, the goals obtained can in turn be divided into more specific sub-goals, which can be represented by means
of an AND-OR graph; this will allow a better understanding of the Big Data implementation.
References:
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L. Liu, ‘Security and Privacy Requirements Engineering Revisited in the Big Data
Era’, in 2016 IEEE 24th International Requirements Engineering Conference Workshops (REW), 2016, pp.
55–55.
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H. Eridaputra, B. Hendradjaya, and W. Danar Sunindyo, ‘Modeling the requirements
for big data application using goal oriented approach’, in 2014 International Conference on Data and Software
Engineering (ICODSE), 2014, pp. 1–6.
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G. Park, L. Chung, L. Zhao, and S. Supakkul, ‘A Goal-Oriented Big Data Analytics
Framework for Aligning with Business’, in 2017 IEEE Third International Conference on Big Data Computing
Service and Applications (BigDataService), 2017, pp. 31–40.
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N. Al-Najran and A. Dahanayake, ‘A Requirements Specification Framework for Big
Data Collection and Capture’, in New Trends in Databases and Information Systems: ADBIS 2015 Short Papers and
Workshops, BigDap, DCSA, GID, MEBIS, OAIS, SW4CH, WISARD, Poitiers, France, September 8-11, 2015. Proceedings,
T. Morzy, P. Valduriez, and L. Bellatreche, Eds. Cham: Springer International Publishing, 2015, pp.
12–19.
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I. Noorwali, D. Arruda, and N. H. Madhavji, ‘Understanding quality requirements
in the context of big data systems’, in Proceedings of the 2nd International Workshop on BIG Data Software
Engineering, Austin, Texas, 2016, pp. 76–79.
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