Nowadays, the similarity search in multimedia databases is performed through similarity queries explicitly specified by users. The queries return a certain part of the database that is relevant to the user specified query parameters. However, this approach suffers in case the user does not know how to specify the query, or actually she/he only wants to know what the database contains in the whole picture. In such case non-standard access to data is more appropriate, e.g., the exploration of a multimedia database.
During the exploration process the user gains a complex idea of all the stored data rather than a particular part of database returned as the result of some similarity query. In the complex view the user is osupported in browsing the space of multimedia data (typically by multi-touch device, provided by modern technologies, e.g., iPad) and that results in stream of similarity queries. For a convenient user-friendly browsing, the exploration system has to evaluate these queries promptly, which is not guaranteed in case of standard query processing (even approximate). Hence, the goal of this project is to propose and implement access methods that provide functionality of real-time similarity retrieval, thus founding fundamentals for user-friendly exploration of multimedia databases.

Similarity search is becoming popular in even more disciplines, such as multimedia databases, bioinformatics, data mining, or social networks. The large-scale search engines for such data are mostly based on models involving low-level features and simple similarity functions. There also exist complex models employing local features and higher-level similarities which provide higher retrieval effectiveness. An application of complex models, however, is not feasible at large scale due to insufficient portfolio of indexing techniques enabling fast search.

The volume of unstructured databases grows extremely whereas its annotation is problematic. The similarity search concept based on a similarity function defined for each pair of database objects is more suitable for this kind of data. The similarity is usually modelled by a distance function satisfying metric axioms, which allows efficient indexing. However, metric axioms can be very restrictive for domain experts who may prefer non-metric functions. Hence database experts have to solve this problem by converting non-metric functions to metric ones or by developing new types of non-metric indexing methods.

One of the areas where metric/non-metric similarity searching is used is computational proteomics. During the determination of the biological function of an "unknown" protein, retrieval of "known" proteins with similar structures (and thus probably with similar function) is very useful. Moreover, fast and cheap determination of protein structures is also an open problem. From database point of view, it is possible to use databases of known protein structures to address this problem. In this approach, sequence-structure similarity functions are used to obtain structures that can be similar to the searched structure of the protein.

Our goal is developing high-quality structure and sequence-structure similarity functions and methods for their indexing.

Current data processing applications use data with considerably less structure and much less precise queries than traditional database systems. The multimedia data, like images or videos, that offer query-by-example search, are a typical example. Such data can neither be ordered in a canonical manner nor meaningfully searched by precise database queries that would return exact matches. This novel situation is what has given rise to a similarity searching. The most general approach to the similarity search, still allowing construction of index structures, is modeled in metric space. Here an important issue is the efficiency - we need to achieve fast query response over huge volumes of data. During last two decades there have been developed many metric access methods and indexing structures, however, they mostly cannot scale up with the exponential growth of multimedia data volumes we encounter during last years. A way to compete this enormous growth is to design parallel and distributed solutions, either as an extension of the traditional centralized indexing techniques, or completely new ones, where the parallelism/distribution are inherent indexing properties. Hence, the goal of the proposed project is the design and implementation of parallel and distributed indexing techniques and comparison with existing centralized solutions.