EGNAT: A Fully Dynamic Metric Access Method for Secondary Memory

Abstract

We introduce a novel metric space search data structure called EGNAT, which is fully dynamic and designed for secondary memory. The EGNAT is based on Brin's GNAT static index, and partitions the space according to hyperplanes. The EGNAT implements deletions using a novel technique dubbed Ghost Hyperplanes, which is of independent interest for other metric space indexes. We show experimentally that the EGNAT is competitive with the M-tree, the baseline for this scenario.