Software architectures for big data: a systematic literature review
Big Data systems are often composed of information extraction, preprocessing, processing, ingestion and integration, data analysis, interface and visualization components. Different big data systems will have different requirements and as such apply different architecture design configurations. Hence a proper architecture for the big data system is important to achieve the provided requirements. Yet, although many different concerns in big data systems are addressed the notion of architecture seems to be more implicit. In this paper we aim to discuss the software architectures for big data systems considering architectural concerns of the stakeholders aligned with the quality attributes. A systematic literature review method is followed implementing a multiple-phased study selection process screening the literature in significant journals and conference proceedings.
C. Salma, B.Tekinerdogan, I. N. Athanasiadis, Software architectures for big data: a systematic literature review, Big Data Analytics, 5:5, 2020, doi:10.1186/s41044-020-00045-1.
You might also enjoy (View all publications)
- Simulating adaptation strategies to offset potential impacts of climate variability and change on maize yields in Embu County, Kenya
- Reuse of process-based models: automatic transformation into many programming languages and simulation platforms
- Advances and New Trends in Environmental Informatics. Digital Twins for Sustainability