To run proper Big Data Analytics, small and medium enterprises (SMEs) need to acquire expertise, hardware and software, which often translates to relevant initial investments for activities not directly connected to the company's business. To reduce such investments, the TOREADOR project proposes a Big Data Analytics framework which supports users in devising their own Big Data solutions by keeping the inherent costs at a minimum, and leveraging pre-existent knowledge and expertise. Among the objectives of the TOREADOR framework is supporting developers in parallelizing and deploying their Big Data algorithms, in order to develop their own analytics solutions. This paper describes the Code-Based approach, adopted within the TOREADOR framework to parallelize users' algorithms and deploy them on distributed platforms, via the annotation of parallelizable code portions with parallelization primitives. The approach, which relies on the guidance of Parallel Patterns to implement the parallelization, and on Skeletons to automatically build execution and deployment templates, is realized through a source-to-source Compiler, also described in the present paper.

The highly heterogeneous environment of IoT (Internet of Things) and Smart cities guarantees that every stakeholder's requirements can be achieved and implemented. However deciding which is the most appropriate sensor to adopt among various similar ones and, even most important, how to use it is not an easy task; a task that, as of today, cannot be done without access to source code or documentation. Moreover heterogeneity in descriptions and definitions complicate sensor interfaces comparison and selection, and may create interoperability and portability problems among multiple providers. In this work we aim at providing a Q&A tool that guides the user in the production of a RESTApi sensor's interfaces according to the OpenAPI specification. The automatically produced ad-hoc specification is used as a base for defining sensors' interfaces equivalence and similarity using natural language processing techniques.

The development of a complex software system involves many actors, whose skills and knowledge are very heterogeneous. The requirements representation made using the Business Process notations is far more understandable than the classic UML representation, at least for Business experts, as it allows to investigate the system from other points of view which are not merely connected the development of the software itself. However, it is not possible to completely disregard UML representations, as they catch software design information which would be lost otherwise. In order to use both representations as basic tools through the whole software development life cycle, a key point is to ensure that they can be easily combined together to facilitate the development of the system software assets. In this paper we address this interoperability scope and provide a unified semantic representation, capable of covering every aspect of software development life cycle and of bonding the different Business and Software development points of view, from the requirements definition to the actual implementation of the source code, including the migration of applications to the Cloud.

Business Process management has attracted the attention of companies and organizations, which have heavily invested in the research and development of tools and standards for process representation and business requirement formalization. Several tools have been produced for the representation and management of Business processes, and many companies have designed their enterprise models using business process standards. In this paper we analyse different approaches to enrich the existing standards, in particular the Business Process Model Notation (BPMN), with the semantic information provided by OWL ontologies. We also discuss the motivations behind the need of a semantic annotation for Business Processes and provide an overview of existing research efforts and results.