Technological changes have reduced the cost of creating, capturing, managing and storing information to a sixth of what it was in 2005. This allowed a scale change in the size and distribution of data, the number of connected devices, and the number of users. Data can be numerical data coming from sensors, scientiﬁc data or personal data coming from heterogeneous and largely distributed data sources. They cannot be handled anymore by centralized data management systems with pre-established schemas. Modern data and knowledge management systems requires new data models, new services and algorithms that must be largely distributed and deployed over diﬀerent types of large scale systems (grids, peer-to-peer networks, sensor networks, web infrastructures). The HADAS group has revisited and extended standard database systems to deal with dynamic and distributed data, to define data management systems as infrastructure comprise of distributed data series or composing data services for handling autonomy, dynamic behavior and heterogeneity of both users and data sources. The activities of the group focuses on:
- Accessing data in large-scale systems: this aspect concerns query optimization in distributed and dynamic systems ;
- Composing data services in a dynamic way: we investigate models, algorithms and tools for coordinating services with non functional properties (contracts) and for providing access to heterogeneous data coming from services ;
We collaborate with other laboratories in several ANR projects and also with industry.
Results of our research have direct impact on applications dealing with huge amounts of data and resources largely distributed in pervasive environments, such as data spaces, smart grids and smart buildings, etc.