Big Data Reference Architectures, a Systematic Literature Review
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The Association for Information Systems (AIS)
Abstract
Today, we live in a world that produces data at an unprecedented rate. The significant amount of data has raised lots of attention and many strive to harness the power of this new material. In the same direction, academics and practitioners have considered means through which they can incorporate data-driven functions and explore patterns that were otherwise unknown. This has led to a concept called Big Data. Big Data is a field that deals with data sets that are too large and complex for traditional approaches to handle. Technical matters are fundamentally critical, but what is even more necessary, is an architecture that supports the orchestration of Big Data systems; an image of the system providing with clear understanding of different elements and their interdependencies. Reference architectures aid in defining the body of system and its key components, relationships, behaviors, patterns and limitations. This study provides an in-depth review of Big Data Reference Architectures by applying a systematic literature review. The study demonstrates a synthesis of high-quality research to offer indications of new trends. The study contributes to the body of knowledge on the principles of Reference Architectures, the current state of Big Data Reference Architectures, and their limitations.Description
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Ataei, Pouya and Litchfield, Alan T., "Big Data Reference Architectures, a systematic literature review" (2020). ACIS 2020 Proceedings. 30.
https://aisel.aisnet.org/acis2020/30
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© 2020 authors. This is an open-access article licensed under a Creative Commons Attribution-NonCommercial 3.0 New Zealand, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and ACIS are credited.