User-defined data types such as intervals require specialized access methods to be efficiently searched and queried. As database implementors cannot provide appropriate index structures and query processing methods for each conceivable data type, present-day object-relational database systems offer extensible indexing frameworks that enable developers to extend the set of built-in index structures by custom access methods. Although these frameworks permit a seamless integration of user-defined indexing techniques into query processing they do not facilitate the actual implementation of the access method itself. In order to leverage the applicability of indexing frameworks, relational access methods such as the Relational Interval Tree (RI-tree), an efficient index structure to process interval intersection queries, mainly rely on the functionality, robustness and performance of built-in indexes, thus simplifying the index implementation significantly. To investigate the behavior and performance of the recently released IBM DB2 indexing framework we use this interface to integrate the RI-tree into the DB2 server. The standard implementation of the RI-tree, however, does not fit to the narrow corset of the DB2 framework which is restricted to the use of a single index only. We therefore present our adaptation of the original two-tree technique to the single index constraint as well as an approximate adaptation which conceptually only needs a single index. As experimental results with interval intersection queries show, the plugged-in access methods deliver excellent performance compared to other techniques.

Recent years have revealed a growing importance of Virtual Reality (VR) visualization techniques which offer comfortable means to enable users to interactively explore 3D data sets. Particularly in the field of computational fluid dynamics (CFD), the rapidly increasing size of data sets with complex geometric and supplementary scalar information requires new out-of-core solutions for fast isosurface extraction and other CFD post-processing tasks. Whereas spatial access methods overcome the limitations of main memory size and support fast data selection, their VR support needs to be improved. Firstly, interactive users strongly depend on quick first views of the regions in their view direction and, secondly, they require quick relevant views even when they change their view point or view direction.We develop novel view-dependent extensions for access methods which support static and dynamic scenarios. Our new human vision-oriented distance function defines an adjusted order of appearance for data objects in the visualization space and, thus, supports quick first views. By a novel incremental concept of view-dependent result streaming which interactively follows dynamic changes of users' viewpoints and view directions, we provide a high degree of interactivity and mobility in VR environments. Our integration into the new index based graphics data server "IndeGS" proves the efficiency of our techniques in the context of post-processing CFD data with dynamically interacting users.

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