Big data services have to face critical challenges from a data management perspective due to strong impact of the data volume, velocity, variety and veracity. Data variety is characterized by a richer diversity of data sources in terms of types, shapes, and sizes, which make data analysis very challenging. Data volume and velocity require solutions able to efficiently deal and scale with very large amount of data (e.g., tera/petabyte order). Data veracity requires to address the uncertainty of data with solutions able to identify and address data quality issues.
Nowadays, multiple classes of Big Data systems are emerging, making hard to have a one-size-fits-all Big Data solution. The level of integration of different Big Data systems is also very low, raising a strong barrier to address Big Data scenarios where multiple aspects have to be faced at the same time. Most Big Data solutions have weak native support for security, privacy and QoS, by not considering security and privacy from the process of data collection, to storage management, and to the application of data analytics operations according to specific policies.
EUBra-BIGSEA provides an integrated, elastic and dynamic fast and Big Data cloud platform to address knowledge discovery by tackling data volume, variety, velocity and veracity issues as well as privacy, security and QoS challenges.
The project will integrate Big Data technologies to support:
Fast data analysis over continuous streams.
Data mining and machine learning.
OLAP-based Big Data analytics
The proposed integrated platform will allow the user to specify applications that combine different types of data and processing elements and instantiate them in a cloud environment. Quality of service, in terms of performance, and security/privacy will be considered along all stages of the development of data analytics applications, integrating security extensions to ensure guarantees along all data movement and processing tasks.