A Tutorial on Learned Multi-dimensional Indexes
A Tutorial on Learned Multi-dimensional Indexes
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DOI：
10.1145/3397536.3426358
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Abdullah Al-Mamun;Hao Wu;W. Aref
中科院分区：
文献类型：
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作者：
Abdullah Al-Mamun;Hao Wu;W. Aref
最近，机器学习（简称ML）已成功应用于数据库索引。学习索引的初步实验表明，与传统数据库相比，具有更好的搜索性能和更低的空间需求。人们已经探索了许多尝试将学习索引扩展到多维空间。这使得学习索引可能适用于空间数据库。本教程的目标是提供单维和多维空间中学习索引的最新覆盖范围。本教程涵盖超过 25 个学习索引。本教程通过分类法在学习索引空间中导航，该分类法有助于在单维和多维空间中对所涵盖的学习索引进行分类。
Recently, Machine Learning (ML, for short) has been successfully applied to database indexing. Initial experimentation on Learned Indexes has demonstrated better search performance and lower space requirements than their traditional database counterparts. Numerous attempts have been explored to extend learned indexes to the multi-dimensional space. This makes learned indexes potentially suitable for spatial databases. The goal of this tutorial is to provide up-to-date coverage of learned indexes both in the single and multi-dimensional spaces. The tutorial covers over 25 learned indexes. The tutorial navigates through the space of learned indexes through a taxonomy that helps classify the covered learned indexes both in the single and multi-dimensional spaces.