The development, design, manufacturing and maintenance of modern engineering products is a very expensive and complex task. Shorter product cycles and a greater diversity of models are becoming decisive competitive factors in the hard-fought automobile and plane market. In order to support engineers to create complex products when being pressed for time, systems are required which answer collision and similarity queries effectively and efficiently. In order to achieve industrial strength, the required specialized functionality has to be integrated into fully-fledged database systems, so that fundamental services of these systems can be fully reused, including transactions, concurrency control and recovery.

This thesis aims at the development of theoretical sound and practical realizable algorithms which effectively and efficiently detect colliding and similar complex spatial objects.

After a short introductory Part I, we look in Part II at different spatial index structures and discuss their integrability into object-relational database systems. Based on this discussion, we present two generic approaches for accelerating collision queries. The first approach exploits available statistical information in order to accelerate the query process. The second approach is based on a cost-based decompositioning of complex spatial objects. In a broad experimental evaluation based on real-world test data sets, we demonstrate the usefulness of the presented techniques which allow interactive query response times even for large data sets of complex objects.

In Part III of the thesis, we discuss several similarity models for spatial objects. We show by means of a new evaluation method that data-partitioning similarity models yield more meaningful results than space-partitioning similarity models. We introduce a very effective similarity model which is based on a new paradigm in similarity search, namely the use of vector set represented objects. In order to guarantee efficient query processing, suitable filters are introduced for accelerating similarity queries on complex spatial objects. Based on clustering and the introduced similarity models we present an industrial prototype which helps the user to navigate through massive data sets.