AXLE: Analytics for Xtremely Large European Data


AXLE focused on automatic scaling of complex analytics, while addressing the full requirements of real data sets. Real data sources have many difficult characteristics. Sources often start small and can grow extremely large as business/initiatives succeed, so the ability to grow seamlessly and automatically is at least as important as managing large data volumes once you know you have them. Extremely large data stores have added concerns such as data quality, privacy, security and auditability. Aspects of the project included:

* Scalability Engineering - Autopartitioning, Compression* Security, Privacy and Audit Techniques* Visual Analytics* Advanced Architectures for Hardware and Software.

Validation was carried out by industrial consortium partners with access to large volumes of private medical data, as well as other wide ranging data from other interested parties.

Software features will be released as commercially-usable open source code, and submitted for wide use as core parts of the PostgreSQL database or Orange visualisation project, or pluggable extensions for those tools.