Model-Based Big Data Analytics-as-a-Service: Take Big Data to the Next Level

Abstract

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.

A trust assurance technique for Internet of things based on human behavior compliance

“…), as well as the process of configuring and training the behavioral policy, are out of the scope of this paper. Again, we use the approach in 13 to produce a technologydependent Big Data computation  + ( + , resp.) as a usable instance of the technology-independent Big Data computation  (, resp.)…”

Towards Specification of a Software Architecture for Cross-Sectoral Big Data Applications

“…In addition, standards for storing and accessing data are being defined [5]. To address the above challenges, several Big Data software architectures have been described, e.g., [6], [2], and [7].…”

“…Despite significant work on defining Big Data software architectures, there are still several gaps that need to be bridged. As pointed in [2], Big Data solutions are usually developed bottom-up, and it is often technologies and not user requirements that drive application development. Reference architectures [10], [11] attempt to provide generic views and solutions, i.e., generalize and harmonize requirements, concerns, etc.…”

Empowering Owners with Control in Digital Data Markets

“…In our hyper-connected society, data are continuously generated at the astonishing pace of quintillions of bytes each day, and the trend is forecasted to grow in the near future also due to the incredible diffusion of the IoT technology. These data, often related to individuals, have a huge value for creating knowledge [1] (data is oftentimes referred to as the oil of the future), and this has fostered a vision towards the development of digital data markets, where data are monetized and traded. In the current scenario, the value is most of the times cashed by a few big players on the market, which have a revenue in collecting and trading data.…”