Use of permutation prefixes for efficient and scalable approximate similarity search

We present the Permutation Prefix Index (PP-Index), an index data structure that allows to perform efficient approximate similarity search. The PP-Index belongs to the family of the permutation-based indexes, which are based on representing any indexed object with ``its view of the surrounding world'', i.e., a list of the elements of a set of reference objects sorted by their distance order with respect to the indexed object. In its basic formulation, the PP-Index is strongly biased toward efficiency. We show how the effectiveness can easily reach optimal levels just by adopting two ``boosting'' strategies: multiple index search and multiple query search, which both have nice parallelization properties. We study both the efficiency and the effectiveness properties of the PP-Index, experimenting with collections of sizes up to one hundred million objects, represented in a very high-dimensional similarity space.