  For complex data types such as multimedia, traditional data management
methods are not suitable. Instead of attribute matching approaches, access
methods based on object similarity are becoming popular. Recently, this
resulted in an intensive research of indexing and searching methods for the
similarity-based retrieval. Nowadays, many efficient methods are already
available, but using them to build an actual search system still requires
specialists that tune the methods and build the system manually. Several
attempts have already been made to provide a more convenient high-level
interface in a form of query languages for such systems, but these are limited
to support only basic similarity queries. In this paper, we propose a new
language that allows to formulate content-based queries in a flexible way,
taking into account the functionality offered by a particular search engine in
use. To ensure this, the language is based on a general data model with an
abstract set of operations. Consequently, the language supports various
advanced query operations such as similarity joins, reverse nearest neighbor
queries, or distinct kNN queries, as well as multi-object and multi-modal
queries. The language is primarily designed to be used with the MESSIF
framework for content-based searching but can be employed by other retrieval
systems as well.
