all AI news
A Survey of Learned Indexes for the Multi-dimensional Space
March 12, 2024, 4:43 a.m. | Abdullah Al-Mamun, Hao Wu, Qiyang He, Jianguo Wang, Walid G. Aref
cs.LG updates on arXiv.org arxiv.org
Abstract: A recent research trend involves treating database index structures as Machine Learning (ML) models. In this domain, single or multiple ML models are trained to learn the mapping from keys to positions inside a data set. This class of indexes is known as "Learned Indexes." Learned indexes have demonstrated improved search performance and reduced space requirements for one-dimensional data. The concept of one-dimensional learned indexes has naturally been extended to multi-dimensional (e.g., spatial) data, leading …
abstract arxiv class cs.db cs.lg data database data set domain index inside keys learn machine machine learning mapping ml models multiple research set space survey trend type
More from arxiv.org / cs.LG updates on arXiv.org
Digital Over-the-Air Federated Learning in Multi-Antenna Systems
2 days, 12 hours ago |
arxiv.org
Bagging Provides Assumption-free Stability
2 days, 12 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
Research Scientist, Demography and Survey Science, University Grad
@ Meta | Menlo Park, CA | New York City
Computer Vision Engineer, XR
@ Meta | Burlingame, CA