The main contribution of this paper is to design an Information Retrieval
(IR) technique based on Algorithmic Information Theory (using the Normalized
Compression Distance- NCD), statistical techniques (outliers), and novel
organization of data base structure. The paper shows how they can be integrated
to retrieve information from generic databases using long (text-based) queries.
Two important problems are analyzed in the paper. On the one hand, how to
detect "false positives" when the distance among the documents is very low and
there is actual similarity. On the other hand, we propose a way to structure a
document database which similarities distance estimation depends on the length
of the selected text. Finally, the experimental evaluations that have been
carried out to study previous problems are shown.Comment: Submitted to 2008 IEEE Information Theory Workshop (6 pages, 6
figures