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

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.

Privacy-aware Big Data Analytics as a service for public health policies in smart cities

“…Our work aims to suggest an improved way to develop public health policies for smart cities, by adopting an approach based on Big Data and supporting the design of privacy-compliant policies. This work extends the work of the EVO-TION H2020 project 4 , which is focused on the definition of public health policies 45 related to hearing loss [3], to a general public health scenario in the context of smart cities. More specifically, it extends a typical public health policy making process towards a Big Data-assisted process, driven by a privacy-aware analysis and processing of dwellers' personal data.…”

“…More specifically, it extends a typical public health policy making process towards a Big Data-assisted process, driven by a privacy-aware analysis and processing of dwellers' personal data. Our approach also adopts TORE-ADOR Big Data Analytics framework [4] to support policy making and extend 50 it to include a semi-automated compliance assessment of GDPR requirements. The contribution of this paper is therefore threefold: i) to formalize a Privacy Compliance Assessment based on GDPR privacy requirements for public health policy making process, ii) to improve on traditional public policy making processes towards Big Data-assisted public policy making, iii) to define a 60 privacy-aware Big Data campaign supporting policy makers in the definition of privacy-aware public policies.…”

“…The contribution of this paper is therefore threefold: i) to formalize a Privacy Compliance Assessment based on GDPR privacy requirements for public health policy making process, ii) to improve on traditional public policy making processes towards Big Data-assisted public policy making, iii) to define a 60 privacy-aware Big Data campaign supporting policy makers in the definition of privacy-aware public policies. This work develops on our previous works [4,3] by providing i) a refined definition of the Big Data-assisted public policy making process for smart cities, ii) privacy-aware Big Data analytics supporting the public policy making process, and iii) a concrete scenario of application and a case study.…”

“…Current approaches and technologies for Big Data analytics in fact suffer from two main drawbacks: i) they are often seen as black boxes that hide workflows and computations in complex services, ii) the availability of many different and non-standard solutions makes Big Data management difficult for not.expert users.500Our goal is therefore to provide an approach that addresses the above drawbacks. To this aim, we rely on the Model-Based Big Data Analytics as a Service (MBDAaaS) approach in[4], where the policy makers, with the assistance of Big Data consultants, declaratively define the goals to be achieved by Big Data campaigns and such that they accomplish the defined public policies, while505 smarter engines manage Big Data platform deployment and analytics execution. MBDAaaS builds on a model-driven engineering paradigm [11] and decouples high-level goals of a Big Data campaign from low-level details of the Big Data architecture.…”

Big Data Assurance Evaluation: An SLA-Based Approach

“…This scenario, however, collides with the lack of expert users having the skills to implement a sound Big Data campaign and retrieve meaningful results. Following this issue, in the last few years, some effort has been done in the definition of techniques supporting the concept of Big Data Analyticsas-a-Service [3], [4], where high-level requirements of the users are transformed in Big Data workflows that can be executed on the target Big Data platform.…”