Presentation

Team presentation

Atlas-GDD is located at Laboratoire d'Informatique de Nantes Atlantique (LINA) and involves researchers from INRIA and the University of Nantes working on data management, 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.

Research directions

Distributed data management

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 the following project:

Atlas P2P Architecture (APPA:) APPA is a peer-to-peer data management system, with advanced functions for data replication, query processing and data integration.