MESSIF: Metric similarity search implementation framework

Abstract

The similarity search has become a fundamental computational task in many applications. One of the mathematical models of the similarity - the metric space - has drawn attention of many researchers resulting in several sophisticated metric-indexing techniques. An important part of a research in this area is typically a prototype implementation and subsequent experimental evaluation of the proposed data structure. This paper describes an implementation framework called MESSIF that eases the task of building such prototypes. It provides a number of modules from basic storage management, over a wide support for distributed processing, to automatic collecting of performance statistics. Due to its open and modular design it is also easy to implement additional modules, if necessary. The MESSIF also offers several readyto-use generic clients that allow to control and test the index structures. © Springer-Verlag Berlin Heidelberg 2007.