Communication Dans Un Congrès Année : 2017

Big Data Analytic Approaches Classification

Résumé

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 howour proposed generic architecture can be used to decide which Big Data analytic approach is suitable in the context of several use cases.

Dates et versions

hal-02098311 , version 1 (12-04-2019)

Identifiants

Citer

Yudith Cardinale, Sonia Guehis, Marta Rukoz. Big Data Analytic Approaches Classification. 12th International Conference on Software Technologies (ICSOFT 2017), Jul 2017, Madrid, Spain. pp.151-162, ⟨10.5220/0006437801510162⟩. ⟨hal-02098311⟩
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