Indexing is an effective way to support efficient query processing in large databases. Recently the concept of learned index, which replaces or complements traditional index structures with machine learning models, has been actively explored to reduce storage and search costs. However, accurate and efficient similarity query processing in high-dimensional metric spaces remains to be an open challenge. In this paper, we propose a novel indexing approach called LIMS that uses data clustering, pivot-based data transformation techniques and learned indexes to support efficient similarity query processing in metric spaces. In LIMS, the underlying data is partitioned into clusters such that each cluster follows a relatively uniform data distribution. Data redistribution is achieved by utilizing a small number of pivots for each cluster. Similar data are mapped into compact regions and the mapped values are totally ordinal. Machine learning models are developed to approximate the position of each data record on disk. Efficient algorithms are designed for processing range queries and nearest neighbor queries based on LIMS, and for index maintenance with dynamic updates. Extensive experiments on real-world and synthetic datasets demonstrate the superiority of LIMS compared with traditional indexes and state-of-the-art learned indexes.

翻译：最近,已经积极探索了以机器学习模型取代或补充传统索引结构的学习指数概念,以减少存储和搜索费用;然而,高维度计量空间的准确和高效相似查询处理仍是一个开放的挑战。在本文件中,我们提议采用称为LIMS的新颖索引方法,使用数据群集、基于主轴的数据转换技术和学习指数,支持在计量空间进行高效的类似查询处理。在LIMS中,基本数据被分割成集群,使每个组群都遵循相对一致的数据分配。数据重新分配是通过利用每个组群的少量支流来实现的。类似的数据被映射到紧凑区域,所绘制的数值是完全随机的。开发了机器学习模型,以接近每个数据记录在磁盘上的位置。高效的算法是为了处理范围查询和基于LIMS的近邻查询,并用动态更新来维持索引。关于现实世界和合成数据集的广泛实验表明LIMS的优势与传统索引和州级所学指数的优势。