Towards Efficient Indexing of Arbitrary Similarity

The popularity of similarity search expanded with the increased interest in multimedia databases, bioinformatics, or social networks, and with the growing number of users trying to find information in huge collections of unstructured data. During the exploration, the users handle database objects in different ways based on the utilized similarity models, ranging from simple to complex models. Efficient indexing techniques for similarity search are required especially for growing databases. In this paper, we study implementation possibilities of the recently announced theoretical framework SIMDEX, the task of which is to algorithmically explore a given similarity space and find possibilities for efficient indexing. Instead of a fixed set of indexing properties, such as metric space axioms, SIMDEX aims to seek for alternative properties that are valid in a particular similarity model (database) and, at the same time, provide efficient indexing. In particular, we propose to implement the fundamental parts of SIMDEX by means of the genetic programming (GP) which we expect will provide highquality resulting set of expressions (axioms) useful for indexing.