Abstrakt
The increasing amount of available unstructured content together with the growing number of large non-relational databases put more emphasis on the \emph{content-based retrieval} and precisely on the area of similarity searching. Although there exist several indexing methods for efficient querying, not all of them are best-suited for arbitrary similarity models.
Having a metric space, we can easily apply metric access methods but for nonmetric models which typically better describe similarities between generally unstructured objects the situation is a little bit more complicated. To address this challenge, we introduce {\bf SIMDEX}, the universal framework that is capable of finding alternative indexing methods that will serve for efficient yet effective similarity searching for any similarity model.
Using trivial or more advanced methods for the incremental exploration of possible indexing techniques, we are able to find alternative methods to the widely used metric space model paradigm. Through experimental evaluations, we validate our approach and show how it outperforms the known indexing methods.