Section: Overall Objectives
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 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. Secondly, data management systems need to scale up to support large-distributed systems (cluster systems, P2P systems) and deal with both fixed and mobile clients. In a highly distributed context, data sources are typically in high number, 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.
The general problem can be summarized as complex data management in distributed systems. The Atlas project-team addresses this problem with the objective of designing and validating new solutions with significant advantages in functionality and performance. To tackle this objective, we separate the problem along four main dimensions which we address in four themes. The theme ``database summaries'' addresses the issues of data abstraction from large size databases. The theme ``model management'' addresses the issues of data abstraction from complexity. The theme ``multimedia data management'' deals with efficient and personalised access to multimedia data. Finally, the theme ``distributed data management'' addresses the problems of data replication and distributed query processing with complex data.
These dimensions are not independent and we foster cross-fertilization between themes. Examples of inter-theme research activities are: multimedia database summaries, multimedia data management in cluster systems, database summaries in P2P systems, and model management applied to distributed data integration.