|
|
| Research teams | Cooperative Initiatives | International Scientific Collaboration | Researchers' News |
|
| About the project : Project-Team site Activity report Videos and photos Research reports Theses Theme : Sym Symbolic systems Rennes - Bretagne Atlantique research center |
ATLAS is a project-team, created at INRIA-Rennes in january 2003. The team is located at Institut de Recherche en Informatique de Nantes (IRIN) and involves researchers from INRIA and the University of Nantes working on data management, multimedia and distributed systems.
Today's hard problems in data management go well beyond the traditional context of Database Management Systems (DBMS). These problems stem from significant evolutions of data, systems and applications. First, data have become much richer and more complex in terms of formats (e.g. multimedia objects), structures (e.g. semi-structured documents), content (e.g. incomplete or imprecise data), size (e.g. very large volumes), and associated semantics (e.g. metadata, code). The management of such data makes it hard to develop data-intensive applications and creates hard performance problems. Second, data management systems need to scale up to support large-distributed systems (such as the Internet or cluster systems) and deal with both fixed and mobile clients. In a highly distributed context, data sources are typically in high numbers, autonomous and heterogeneous, thereby making data integration difficult. Third, this combined evolution of data and systems gives rise to new, typically complex, applications with ubiquitous, on-line data access: virtual libraries, virtual stores, global catalogs, services for personal content management, services for mobile data management, etc.
Models and summaries
DBMS has become a very mature technology that is ubiquitous in information systems. Over time, the extensive use of DBMS technology has had major consequences in large organizations: the production of very large databases, the production of heterogeneous databases, and the increasing requirement of diverse applications to access those very large, heterogeneous databases. This creates difficult technical problems which get worse as DBMS technology improves and is more able to produce very large, heterogeneous databases. We believe a common answer to these problems must rest on a generalization of the principle of data independence, applied to instances (e.g. relational tuples) for very large databases as well as to data descriptions (e.g. schemas) for heterogeneous databases and applications. Therefore, we pursue two independent research actions, one on summary management and another on model management, and a third federating action on the integration of heterogeneous summaries and models.
Multimedia data management techniques
The ability to store multimedia information in digital form has spurred both demand and offer of new electronic appliances (e.g. DVD players, digital cameras, mobile phones connected to the Web, etc.) and new applications (e.g. interactive video, digital photo album, electronic postcard, distance learning, etc.). The increasing production of digital multimedia data magnifies the traditional problems of multimedia data management and creates new problems such as content personalization and access from mobile devices. The major issues are in the areas of multimedia data modelling, physical storage and indexing as well as query processing with multimedia data. We pursue three research actions: multimedia data integration with heterogeneous descriptors, large scale indexing and retrieval, and access from mobile devices.
Distributed data management techniques
In a large scale distributed system, data sources are typically in high numbers, autonomous (under strict local control) and very heterogeneous in size and complexity. Furthermore, clients can be mobile terminals which can work in disconnected mode and get synchronized from time to time with the databases over the network. Data management in this context offers new research opportunities since traditional distributed database techniques need to scale up while supporting data autonomy and heterogeneity, and clients' mobility. There are different distributed system contexts where we can study these problems, in particular, Internet and clusters of PC. However, to yield general results, we strive to develop common algorithmic solutions with the right level of abstraction from the context. Thus, we assume a peer-to-peer (P2P) distributed system architecture which is able to scale up. Given a P2P architecture, data consistency and the performance of data access are crucial. To address these general problems, we pursue three complementary research actions: distributed data replication, consistency management of replicated data, and distributed query processing.
