A Scalable Analytics Platform (ASAP)
The ASAP FP7 research project develops a dynamic open-source execution framework for scalable data analytics. The underlying idea is that no single execution model is suitable for all types of tasks, and no single data model (and store) is suitable for all types of data. Complex analytical tasks over multi-engine environments therefore require integrated profiling, modeling, planning and scheduling functions. Addressing these challenges, ASAP pursues four main goals:
- A generic task-parallel programming model in conjunction with a runtime system for execution in the cloud. The runtime will incorporate and advance state-of-the-features including:
- irregular general-purpose computations,
- resource elasticity,
- synchronization, data-transfer, locality and scheduling abstraction,
- ability to handle large sets of irregularly distributed data, and
- A modeling framework that constantly evaluates the cost, quality and performance of available computational resources in order to decide on the most advantageous store, indexing and execution pattern.
- A unique adaptation methodology that will enable analytics experts to amend submitted tasks in later processing stages.
- A real-time visualization engine to show the results of the initiated tasks and queries in an intuitive manner – building on the dashboard of the Media Watch on Climate Change and the faceted search developed for the Climate Resilience Toolkit.