Web Engines are a useful tool for searching information in the Web. But a great part of this
information is non-textual and for that case a metric space is used. A metric space is a set where a
notion of distance (called a metric) between elements of the set is defined. In this paper we present
an efficient parallelization of a pivot-based method devised for this purpose which is called the
Sparse Spatial Selection (SSS) strategy and we compare it with a clustering-based method, a
parallel implementation of the Spatial Approximation Tree (SAT). We show that SAT compares
favourably against the pivot data structures SSS. The experimental results were obtained on a highperformance
cluster and using several metric spaces, that shows load balance parallel strategies
for the SAT. The implementations are built upon the BSP parallel computing model, which shows
efficient performance for this application domain and allows a precise evaluation of algorithms.VIII Workshop de Procesamiento Distribuido y ParaleloRed de Universidades con Carreras en Informática (RedUNCI
