EGNAT: A Fully Dynamic Metric Access Method for Secondary Memory

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.