Is Quantized ANN Search Cursed? Case Study of Quantifying Search and Index Quality

Abstract

Traditional evaluation of an approximate high-dimensional index typically consists of running a benchmark with known ground truth, analyzing the performance in terms of traditional result quality and latency measures, and then comparing those measures to competing index structures. Such analysis can give an overall indication of the suitability of the index for the application that the benchmark represents. When the index inevitably fails to return the sought items for some queries, however, this methodology does not help to explain why the index fails in those cases. Furthermore, when considering many different parameter settings, the process of repeatedly indexing the entire collection is prohibitively time-consuming. In this paper, we define three causes for failures in hierarchical quantized search. We show that the two failure cases that relate to the index can be evaluated and quantified using only the index structure and ground-truth data. In our evaluation, we use eCP, a lightweight algorithm that builds the index hierarchy top-down a priori without any costly segmentation of the dataset, and show that significant insight can be gained into the quality of the index structure, or lack thereof.