\begin{abstract}
The \fonion\ is a robust access method that slices the metric space into disjoint subspaces to provide quick indexing of complex data in the main memory.
However, the \fonion\ only performs \ees\ into its structure, i.e. it does not introduce a technique to build the index considering all elements of the dataset at once.
In this article, we fill this gap. We propose the \hbl\ for \bl\ \fonions.
Performance tests with real-world data with different volumes and dimensionalities showed that the index produced by the \hbl\ is very compact. 
Compared with the \ee, the size of the index reduced from 53.42\% to 71.25\%.
The experiments also showed that the \hbl\ significantly improved range and \knn\ query processing performance.
It required from 13.38\% up to 99.94\% less distance calculations and was 
from 8.57\% up to 99.04\% faster than the \ee.
\end{abstract}

% ACM Computing Classification System categories
\category{Core Database Foundations and Technology}{Access methods and indexing}{Databases}

% Article keywords
\keywords{generalized suffix tree, indexing, similarity search, time series}
%\keywords{JIDM, template}
%  -> \keywords{} (in alphabetical order \keywords{document processing, sequences,
%                      string searching, subsequences, substrings})

\keywords{metric access method, similarity search, bulk-loading, \onion, \fonion}	

