The State of Big Data Reference Architectures: A Systematic Literature Review

aut.relation.endpage113807
aut.relation.journalIEEE Accessen_NZ
aut.relation.startpage113789
aut.relation.volume10en_NZ
aut.researcherLitchfield, Alan
dc.contributor.authorAtaei, Pen_NZ
dc.contributor.authorLitchfield, Aen_NZ
dc.date.accessioned2022-11-23T03:02:19Z
dc.date.available2022-11-23T03:02:19Z
dc.date.copyright2022en_NZ
dc.date.issued2022en_NZ
dc.description.abstractBig Data (BD) is a nascent term emerged to describe large amount of data that comes in different forms from various channels. In modern world, users are the ceaseless generators of structured, semi-structured, and unstructured data that if gleaned and crunched precisely, will reveal game-changing patterns. While the opportunities exist with BD, the unprecedented amount of data has brought traditional approaches to a bottleneck, and the growth of data is outpacing technological and scientific advances in data analytics. It is estimated that approximately 75% of the BD projects have failed within the last decade according to multiple sources. Among the challenges, system development and data architecture are prominent. This paper aims to facilitate BD system development and architecture by conducting a systematic literature review on BD reference architectures (RA). The primary goal is to highlight the state of BD RAs and how they can be helpful for BD system development. The secondary goal is to find all BD RAs, describe the challenges of creating these RA, discuss the common architectural components of these RA and the limitations of these RA. As a result of this work, firstly major concepts about RA are discussed and their applicability to BD system development is depicted. Secondly, 22 BD reference architecture is assessed from academia and practice and their commonalities, challenges, and limitations are identified. The findings gained emerges the understanding that RAs can be an effective artefact to tackle complex BD system development.
dc.identifier.citationIEEE Access, vol. 10, pp. 113789-113807, 2022, doi: 10.1109/ACCESS.2022.3217557
dc.identifier.doi10.1109/access.2022.3217557en_NZ
dc.identifier.issn2169-3536en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/15656
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_NZ
dc.relation.urihttp://dx.doi.org/10.1109/access.2022.3217557en_NZ
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectBig data; Big data reference architectures; Big data architectures; Big data for business; Data analytics; Data engineering; Data-intensive applications; Reference architectures
dc.titleThe State of Big Data Reference Architectures: A Systematic Literature Reviewen_NZ
dc.typeJournal Article
pubs.elements-id483248
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences
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