Dynamic permutation based index for proximity searching

Abstract

Proximity searching consists in retrieving objects from a dataset that are similar to a given query. This kind of tool is an elementary task in different areas, for instance pattern recognition or artificial intelligence. To solve this problem, it is usual to use a metric index. The permutation based index (PBI) is an unbeatable metric technique which needs just few bits for each object in the index. In this paper, we present a dynamic version of the PBI, which supports insertions, deletions and updates, and keeps the effectiveness of the original technique.