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

Claudio A. Ardagna, Valerio Bellandi, Paolo Ceravolo, Ernesto Damiani, Michele Bezzi, Cedric Hebert

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

38 Scopus citations

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.

Original languageBritish English
Title of host publicationProceedings - 2017 IEEE 6th International Congress on Big Data, BigData Congress 2017
EditorsGeorge Karypis, Jia Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages105-112
Number of pages8
ISBN (Electronic)9781538619964
DOIs
StatePublished - 7 Sep 2017
Event6th IEEE International Congress on Big Data, BigData Congress 2017 - Honolulu, United States
Duration: 25 Jun 201730 Jun 2017

Publication series

NameProceedings - 2017 IEEE 6th International Congress on Big Data, BigData Congress 2017

Conference

Conference6th IEEE International Congress on Big Data, BigData Congress 2017
Country/TerritoryUnited States
CityHonolulu
Period25/06/1730/06/17

Keywords

  • Big Data
  • Model-Driven Architecture
  • OWL-S

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