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Fakultät für Mathematik, Informatik und Statistik - Digitale Hochschulschriften der LMU - Teil 01/02 Podcast
Spatial Database Support for Virtual Engineering

Spatial Database Support for Virtual Engineering

Fakultät für Mathematik, Informatik und Statistik - Digitale Hochschulschriften der LMU - Teil 01/02 vor 21 Jahren

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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.