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Authors: Yudith Cardinale 1 ; Sonia Guehis 2 and Marta Rukoz 2

Affiliations: 1 Universidad Simón Bolívar, Venezuela ; 2 Université Paris Nanterre, Université Paris Dauphine, PSL Research University and CNRS, France

ISBN: 978-989-758-262-2

ISSN: 2184-2833

Keyword(s): Big Data Analytic, Analytic Models for Big Data, Analytical Data Management Applications.

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

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 ou r proposed generic architecture can be used to decide which Big Data analytic approach is suitable in the context of several use cases. (More)

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Paper citation in several formats:
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, ISSN 2184-2833, pages 151-162. DOI: 10.5220/0006437801510162

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,},


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

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