Query language for complex similarity queries

Abstract. For complex data types such as multimedia, traditional data management methods are not suitable. Instead of attribute matching ap-proaches, access methods based on object similarity are becoming pop-ular. Recently, this resulted in an intensive research of indexing and searching methods for the similarity-based retrieval. Nowadays, many ef-ficient 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 pro-vide 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 for-mulate 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 frame-work for content-based searching but can be employed by other retrieval systems as well