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.
The Big Data revolution promises to build a data-driven ecosystem where better decisions are supported by enhanced analytics and data management. However, major hurdles still need to be overcome on the road that leads to commoditization and wide adoption of Big Data Analytics (BDA). Big Data complexity is the first factor hampering the full potential of BDA. The opacity and variety of Big Data technologies and computations, in fact, make BDA a failure prone and resource-intensive process, which requires a trial-and-error approach. This problem is even exacerbated by the fact that current solutions to Big Data application development take a bottom-up approach, where the last technology release drives application development. Selection of the best Big Data platform, as well as of the best pipeline to execute analytics, represents then a deal breaker. In this paper, we propose a return to roots by defining a Model-Driven Engineering (MDE) methodology that supports automation of BDA based on model specification. Our approach lets customers declare requirements to be achieved by an abstract Big Data platform and smart engines deploy the Big Data pipeline carrying out the analytics on a specific instance of such platform. Driven by customers' requirements, our methodology is based on an OWLS ontology of Big Data services and on a compiler transforming OWLS service compositions in workflows that can be directly executed on the selected platform. The proposal is experimentally evaluated in a real-world scenario focusing on the threat detection system of SAP.
With the aim of building a "Semantic Web", the content of the documents must be explicitly represented through metadata in order to enable contents-guided search. Our approach is to exploit a standard language (RDF, recommended by W3C) for expressing such metadata and to interpret these metadata in conceptual graphs (CG) in order to exploit querying and inferencing capabilities enabled by CG formalism. The paper presents our mapping of RDF into CG and its interest in the context of the semantic Web 1 .
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