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Authors: Valerie Garises and José G. Quenum

Affiliation: Namibia University of Science and Technology, 13 Jackson Kaujeua, Windhoek and Namibia

Keyword(s): Big Data, Software Architecture Analysis, Software Architecture Evaluation, Software Architectural Patterns.

Related Ontology Subjects/Areas/Topics: Architectural Concepts ; Data Engineering ; Data Management and Quality

Abstract: In this paper, we present a novel evaluation of architectural patterns and software architecture analysis using Architecture-based Tradeoff Analysis Method (ATAM). To facilitate the evaluation, we classify the Big Data intrinsic characteristics into quality attributes. We also categorised existing architectures following architectural patterns. Overall, our evaluation clearly shows that no single architectural pattern is enough to guarantee all the required quality attributes. As such, we recommend a combination of more than one pattern. The net effect of this would be to increase the benefits of each architectural pattern and then support the design of Big Data software architectures with several quality attributes.

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Paper citation in several formats:
Garises, V. and Quenum, J. (2019). An Evaluation of Big Data Architectures. In Proceedings of the 8th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-377-3; ISSN 2184-285X, SciTePress, pages 152-159. DOI: 10.5220/0007840801520159

@conference{data19,
author={Valerie Garises. and José G. Quenum.},
title={An Evaluation of Big Data Architectures},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - DATA},
year={2019},
pages={152-159},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007840801520159},
isbn={978-989-758-377-3},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - DATA
TI - An Evaluation of Big Data Architectures
SN - 978-989-758-377-3
IS - 2184-285X
AU - Garises, V.
AU - Quenum, J.
PY - 2019
SP - 152
EP - 159
DO - 10.5220/0007840801520159
PB - SciTePress

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