Annual Report 2010 - Fachgruppe Informatik an der RWTH Aachen ...

Fast Access to Spatial-Temporal Data
High Dimensional Indexing
Stephan Günnemann, Hardy Kremer
Recent applications demand fast query response times on high dimensional data. For this
purpose index structures were introduced. Existing multidimensional indexes like the R-tree
provide efficient querying for only relatively few dimensions. Therefore we develop new
index structures for efficient retrieval and similarity search.
Due to massive overlap of index descriptors, multidimensional indexes degenerate for high
dimensions and access the entire data by random I/O. Consequently, the efficiency benefits of
indexing are lost. By exploiting inherent properties of the indexed data, our new index
structures, the TS-Tree and the OF-Tree, can index high-dimensional data in an overlap-free
manner; during query processing, powerful pruning via quantized separator and metadata
information greatly reduces the number of pages which have to be accessed, resulting in
substantial speed-up.
Due to the increasing main memory capacity of modern computers, a high percentage of
datasets fits into main memory. We develop novel main memory based index structures that
use individual dimensions for each data object by applying the method of subspace clustering.
By a local selection of dimensions we increase the information content for objects compared
to a global approach; this higher information content enables a better pruning of the search
space.
Figure 12: Representation in data space (left) and data structure (right) of an overlap free R-tree.
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