Toreador Lab


TOREADOR Lab is a framework for Model-driven Design and Deployment of Big Data pipelines supporting accountability, reproducibility, and verifiability of the design specifications.

The TOREADOR Lab aims at providing a platform that supports customers lacking Big Data expertise in the management of BDA and deployment of a full Big Data pipeline. It can be seen as a function that takes as input users’ Big Data goals and preferences, and returns as output a ready-to-be executed Big Data pipeline.

A set of industry-dependent declarative goals can be used by the user in order to drive the subsequent design steps. We argue that identifying a core set of standard declarative goals is an important step towards increasing transparency of the commitments taken by Big Data service providers, as well as the awareness of users adopting a Big Data solution. Declarative goals present a way for measuring or assessing a business goal, such as analytics tasks or regulatory constraints on personal data protection, and are accompanied by Big Data objectives representing the target to be achieved for fulfilling the goal.

Similarly, using a platform-independent representation for specifying Big Data pipelines will give visibility to compositional aspects that often remain internals and invisible when vendor specific solutions are imported. Moreover, the explicit representation of these compositional aspects makes possible to store and reuse a solution increasing accountability and reproducibility. The end-to-end verifiability of specifications is also facilitated by a model-driven approach making specifications indexable and queriable.

Status

Archive

License(s)

Apache License 2.0

Website

Releases / Downloads

VCS repository(ies)

Project leader(s)

Ernesto Damiani , Paolo Ceravolo


OW2 submission