A big-data analytics platform developed under the European Union-funded Trustworthy Model-Aware Analytics Data Platform (Toreador) project should automatically manage all major challenges related to on-demand data preparation.
The framework supports two automated transformations, with the first starting from a machine-readable declarative model which gathers the data-owner goals and results in a technology-independent semantics-aware procedural model defining the executable computation. The second transformation builds on the procedural model to compute a technology-dependent deployment model.
"Our declarative models can interactively collect the business goals of big-data campaigns and allow the TOREADOR toolkit to provide automatic advice on the feasibility of solutions," says project leader Ernesto Damiani. "Our procedural models then provide an innovative description of the big-data analytics computation in the OWL/S semantics-aware standards, and our compilers translate these procedural models into fully executable workflows or even into natively parallelized Python code."
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