An increasing number of database applications have emerged for which efficient and effective support for similarity search is substantial. Particularly, the task of finding similar shapes in 2D and 3D becomes more and more important. Examples for new applications that require the retrieval of similar 3D objects include databases for molecular biology, medical imaging and computer aided design. Hierarchical clustering was shown to be effective for evaluating similarity models. Furthermore, visually analyzing cluster hierarchies helps the user, e.g. an engineer, to find and group similar objects. We present an interactive browsing tool called BOSS (browsing OPTICS-plots for similarity search), which utilizes solid automatic cluster recognition and extraction of meaningful cluster representatives in order to provide the user with significant and quick information.

Similarity search 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 a clear representation of the results can help to reduce the cost of developing and producing new parts by maximizing the reuse of existing parts. In this paper, we adapt two known similarity models to voxelized 3-D CAD data and introduce a new model based on eigenvectors. The experimental evaluation of our three similarity models is based on two real-world test datasets. Furthermore, we introduce hierarchical clustering as a new and effective way to analyse and compare similarity models. We show that both our similarity model as well as our evaluation procedure are suitable for industrial use.

Modern database applications including computer-aided design (CAD), medical imaging, or molecular biology impose new requirements on spatial query processing. Particular problems arise from the need of high resolutions for very large spatial objects, including cars, space stations, planes and industrial plants, and from the design goal to use general purpose database management systems in order to guarantee industrial-strength. In the past two decades, various stand-alone spatial index structures have been proposed but their integration into fully-fledged database systems is problematic. Most of these approaches are based on decomposition of spatial objects leading to replicating index structures. In contrast to common black-and-white decompositions which suffer from the lack of intermediate solutions, we introduce grey approximations as a new and general concept. We demonstrate the benefits of grey approximations in the context of encoding spatial objects by space filling curves resulting in grey interval sequences. Spatial intersection queries are then processed by a filter and refine architecture which, as an important design goal, can purely be expressed by means of the SQL: 1999 standard. Our new High Resolution Indexing (HRI) method can easily be integrated into general purpose DBMSs. The experimental evaluation on real-world test data from car and plane design projects points out that our new concept outperforms competitive techniques that are implementable on top of a standard object-relational DBMS by an order of magnitude with respect to secondary storage space and overall query response time.

The efficient management of interval data represents a core requirement for many temporal and spatial database applications. With the relational interval tree (RI-tree), an efficient access method has been proposed to process interval intersection queries on top of existing object-relational database systems. The paper complements that approach by effective and efficient models to estimate the selectivity and the I/O cost of interval intersection queries in order to guide the cost-based optimizer whether and how to include the RI-tree into the execution plan. By design, the models immediately fit to common extensible indexing/optimization frameworks, and their implementations exploit the built-in statistics facilities of the database server. According to our experimental evaluation on an Oracle database, the average relative error of the estimated cost to the actual cost of index scans ranges from 0% to 23%, depending on the resolution of the persistent statistics and the size of the query objects.

This demonstration presents a spatial database integration for novel CAD applications into off-the-shelf database systems. Spatial queries on even large product databases for digital mockup or haptic rendering are performed at interactive response times.