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We present the ML-Index, a memory-efficient Multidimensional Learned (ML) structure for processing point, KNN and range queries. Using data-dependent reference points, the ML-Index partitions the data and transforms it into one-dimensional values relative to the distance to their closest reference point. Once scaled, the ML-Index utilizes a learned model to efficiently approximate the order of the scaled values. We propose a novel offset scaling method, which provides a function which is more easily learnable compared to the existing scaling method of the iDistance approach. We validate the feasibility and show the supremacy of our approach through a thorough experimental performance comparison using two real-world data sets.

The ML-Index - A Multidimensional, Learned Index for Point, Range, and Nearest-Neighbor Queries.