Big Data Analytic Approaches Classification

Yudith Cardinale, Sonia Guehis, Marta Rukoz

2017

Abstract

Analytical data management applications, affected by the explosion of the amount of generated data in the context of Big Data, are shifting away their analytical databases towards a vast landscape of architectural solutions combining storage techniques, programming models, languages, and tools. To support users in the hard task of deciding which Big Data solution is the most appropriate according to their specific requirements, we propose a generic architecture to classify analytical approaches. We also establish a classification of the existing query languages, based on the facilities provided to access the big data architectures. Moreover, to evaluate different solutions, we propose a set of criteria of comparison, such as OLAP support, scalability, and fault tolerance support. We classify different existing Big Data analytics solutions according to our proposed generic architecture and qualitatively evaluate them in terms of the criteria of comparison. We illustrate how our proposed generic architecture can be used to decide which Big Data analytic approach is suitable in the context of several use cases.

Paper Citation

in Harvard Style

Cardinale Y., Guehis S. and Rukoz M. (2017). Big Data Analytic Approaches Classification . In Proceedings of the 12th International Conference on Software Technologies - Volume 1: ICSOFT, ISBN 978-989-758-262-2, pages 151-162. DOI: 10.5220/0006437801510162

in Bibtex Style

@conference{icsoft17,
author={Yudith Cardinale and Sonia Guehis and Marta Rukoz},
title={Big Data Analytic Approaches Classification},
booktitle={Proceedings of the 12th International Conference on Software Technologies - Volume 1: ICSOFT,},
year={2017},
pages={151-162},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006437801510162},
isbn={978-989-758-262-2},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Software Technologies - Volume 1: ICSOFT,
TI - Big Data Analytic Approaches Classification
SN - 978-989-758-262-2
AU - Cardinale Y.
AU - Guehis S.
AU - Rukoz M.
PY - 2017
SP - 151
EP - 162
DO - 10.5220/0006437801510162