| Database Integration for Virtual Engineering |
| The development, design, manufacturing and maintenance of modern engineering products is a very expensive and complex task. Today, thousands to millions of CAD files of a car or an airplane occupy terabytes of distributed secondary and tertiary storage. The main objective of this project is to develop techniques for effective and efficient management of huge amounts of spatial data. As main application we focus on spatial queries for digital mockup, haptics simulation and document management. |
| Knowledge Discovery on Spatial Objects |
| Similarity search and cluster detection in database systems is becoming an increasingly important task in modern application domains such as multimedia, molecular biology, medical imaging and many others. Especially for CAD applications, suitable similarity models and their intuitive implementation in search engines can help to reduce the cost of developing and producing new parts by maximizing the reuse of existing parts. In this project, we develop and evaluate new approaches to find similar objects. |
| Extensibility in Object-Relational Databases |
| Modern object-relational database systems (ORDBMS) provide frameworks to logically extend the relational data model by complex data types and operators. A robust and efficient data management is essential for off-the-shelf ORDBMS to advance into non-standard application domains as Geographical Information Systems (GIS), Computer Aided Design (CAD), or Enterprise Resource Planning (ERP). The main focus of our research activities lies on the design of relational storage and access methods that enable commercial ORDBMS to deliver the scalability and performance required by many non-standard applications. |
| Temporal Data Management |
| Todays database applications show a growing demand for dynamic management of intervals, particularly for temporal domains. Common approaches require intrusive modifications of the database kernel in order to support temporal queries. By design, our new Relational Interval Tree (patent pending) employs built-in indexes on an as-they-are basis and is naturally supported by any RDBMS or ORDBMS. As our technique generalizes from one-dimensional extended objects to multi-dimensional data spaces, it also serves as a basic component for spatial query processing. |