Scaling Disk-Resident Learned Indexes For Database Systems

本项目旨在研究新的磁盘驻留学习索引算法，通过推进最先进的内存驻留学习建模来存储和处理数据库系统中的数据。该项目预计将在数字存储技术领域产生新的知识，利用新的和有效的技术在大数据的学习索引。这将带来巨大的好处，使现代数据库系统能够随着数据的大规模增长而扩展，提高数据处理的效率，提高利用大数据的项目的有效性，并大大降低澳大利亚数据中心在存储和从数据库检索数据时的能源成本，并降低其碳足迹。

This project aims to investigate new disk-resident learned indexing algorithms to store and process data in database systems by advancing the state-of-the-art in memory-resident learned modeling. This project expects to generate new knowledge in the area of digital storage technologies utilising novel and efficient techniques in learned indexing for big data. This should provide significant benefits to enable modern database systems to scale with the massive growth of data, improve the efficiency of data processing, improve the effectiveness of projects that utilise big data, and dramatically reduce energy costs in Australian data centres when storing and retrieving data from databases and lower their carbon footprints.