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Classifying Big Data Analytic Approaches: A Generic Architecture

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Date
2017
Notes
Communications in Computer and Information Science book series (CCIS, volume 868)
Dewey
Programmation, logiciels, organisation des données
Sujet
Big Data Analytic; Analytic models for big data; Analytical data management applications
DOI
http://dx.doi.org/10.1007/978-3-319-93641-3_13
Conference name
12th International Conference on Software Technologies (ICSOFT 2017)
Conference date
07-2017
Conference city
Madrid
Conference country
Spain
Book title
Software Technologies,12th International Joint Conference (ICSOFT 2017)
Author
Cabello, Enrique; Cardoso, Jorge; Maciaszek, Leszek A.; van Sinderen, Marten
Publisher
Springer International Publishing
Publisher city
Berlin Heidelberg
Year
2017
Pages number
309
ISBN
978-3-319-93640-6
Book URL
10.1007/978-3-319-93641-3
URI
https://basepub.dauphine.fr/handle/123456789/19208
Collections
  • LAMSADE : Publications
Metadata
Show full item record
Author
Cardinale, Yudith
100644 Universidad Simon Bolivar [USB]
Guehis, Sonia
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Rukoz, Marta
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Type
Communication / Conférence
Item number of pages
268-295
Abstract (EN)
The explosion of the huge amount of generated data to be analyzed by several applications, imposes the trend of the moment, the Big Data boom, which in turn causes the existence of a vast landscape of architectural solutions. Non expert users who have to decide which analytical solutions are the most appropriates for their particular constraints and specific requirements in a Big Data context, are today lost, faced with a panoply of disparate and diverse solutions. To support users in this hard selection task, in a previous work, we proposed a generic architecture to classify Big Data Analytical Approaches and a set of criteria of comparison/evaluation. In this paper, we extend our classification architecture to consider more types of Big Data analytic tools and approaches and improve the list of criteria to evaluate them. 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. Additionally, we propose a preliminary design of a decision support system, intended to generate suggestions to users based on such classification and on a qualitative evaluation in terms of previous users experiences, users requirements, nature of the analysis they need, and the set of evaluation criteria.

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