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Zusammenfassung:

Schema Management

Our work on  schema and metadata management focuses on the following areas:

• Schema Matching: finding semantic correspondences between schema elements (Match operator).
• Model Management: a new approach to generic metadata management that manipulates  models and mappings between models using high-level operators.
• Specific metadata issues, e.g., in data warehouses and data-intensive molecular-biological applications.

We implement our approaches in prototypes such as COMA, Rondo and GenMapper.

Web Data Integration Most proposed approaches on data integration rely on the notion of a global schema to provide a unified and consistent view of the underlying data sources. While it has been successful for data warehouses, the effort to integrate new sources usually is high. This makes it difficult for such approaches to scale to many sources. Furthermore, for virtual data integration it is challenging to obtain a good data quality.

Our work on web data integration focusses on dynamic information fusion of data sources available on the web. Similar to the idea of mashups, we want to achieve a fast development of data integration applications by reusing existing services and entity search engines within a workflow-like data integration. Integration workflows are defined using a script language supporting powerful generic operators. See labs.dbs.uni-leipzig.de for current prototypes and implementations