The Big Data revolution has promised to build a data-driven ecosystem where better decisions are supported by enhanced analytics and data management. However, critical issues still need to be solved in the road that leads to commodization of Big Data Analytics, such as the management of Big Data complexity and the protection of data security and privacy. In this paper, we focus on the first issue and propose a methodology based on Model Driven Engineering (MDE) that aims to substantially lower the amount of competences needed in the management of a Big Data pipeline and to support automation of Big Data analytics. The proposal is experimentally evaluated in a real-world scenario: the implementation of novel functionality for Threat Detection Systems.

A Model-Driven Methodology for Big Data Analytics-as-a-Service / C.A. Ardagna, V. Bellandi, P. Ceravolo, E. Damiani, M. Bezzi, C. Hebert - In: Big Data (BigData Congress), 2017 IEEE International Congress on[s.l] : IEEE, 2017. - ISBN 9781538619964. - pp. 105-112 (( Intervento presentato al 6. convegno IEEE International Congress on Big Data, BigData Congress tenutosi a Honolulu nel 2017 [10.1109/BigDataCongress.2017.23].

A Model-Driven Methodology for Big Data Analytics-as-a-Service

C.A. Ardagna;V. Bellandi;P. Ceravolo;E. Damiani;
2017

Abstract

The Big Data revolution has promised to build a data-driven ecosystem where better decisions are supported by enhanced analytics and data management. However, critical issues still need to be solved in the road that leads to commodization of Big Data Analytics, such as the management of Big Data complexity and the protection of data security and privacy. In this paper, we focus on the first issue and propose a methodology based on Model Driven Engineering (MDE) that aims to substantially lower the amount of competences needed in the management of a Big Data pipeline and to support automation of Big Data analytics. The proposal is experimentally evaluated in a real-world scenario: the implementation of novel functionality for Threat Detection Systems.
Big Data; Model-Driven Architecture; OWL-S; Information Systems; Artificial Intelligence; Computer Networks and Communications; Information Systems and Management
Settore INF/01 - Informatica
2017
IEEE Computer Society Technical Committee on Service Computing (TC-SVC)
IEEE Computer Society Technical Committee on Service Society (TC-SS)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/554831
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