LISA: A learned index structure for spatial data
In spatial query processing, the popular index R-tree may incur large storage consumption
and high IO cost. Inspired by the recent learned index [17] that replaces B-tree with machine …
and high IO cost. Inspired by the recent learned index [17] that replaces B-tree with machine …
Tsunami: A learned multi-dimensional index for correlated data and skewed workloads
Filtering data based on predicates is one of the most fundamental operations for any modern
data warehouse. Techniques to accelerate the execution of filter expressions include …
data warehouse. Techniques to accelerate the execution of filter expressions include …
Database meets artificial intelligence: A survey
Database and Artificial Intelligence (AI) can benefit from each other. On one hand, AI can
make database more intelligent (AI4DB). For example, traditional empirical database …
make database more intelligent (AI4DB). For example, traditional empirical database …
Effectively learning spatial indices
Machine learning, especially deep learning, is used increasingly to enable better solutions
for data management tasks previously solved by other means, including database indexing …
for data management tasks previously solved by other means, including database indexing …
Defining and designing spatial queries: the role of spatial relationships
A Chaves Carniel - Geo-spatial Information Science, 2024 - Taylor & Francis
Spatial relationships are core components in the design and definition of spatial queries. A
spatial relationship determines how two or more spatial objects are related or connected in …
spatial relationship determines how two or more spatial objects are related or connected in …
APEX: a high-performance learned index on persistent memory
The recently released persistent memory (PM) offers high performance, persistence, and is
cheaper than DRAM. This opens up new possibilities for indexes that operate and persist …
cheaper than DRAM. This opens up new possibilities for indexes that operate and persist …
The rlr-tree: A reinforcement learning based r-tree for spatial data
Learned indexes have been proposed to replace classic index structures like B-Tree with
machine learning (ML) models. They require to replace both the indexes and query …
machine learning (ML) models. They require to replace both the indexes and query …
A tutorial on learned multi-dimensional indexes
A Al-Mamun, H Wu, WG Aref - … of the 28th International Conference on …, 2020 - dl.acm.org
Recently, Machine Learning (ML, for short) has been successfully applied to database
indexing. Initial experimentation on Learned Indexes has demonstrated better search …
indexing. Initial experimentation on Learned Indexes has demonstrated better search …
[PDF][PDF] The ML-Index: A Multidimensional, Learned Index for Point, Range, and Nearest-Neighbor Queries.
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 …
processing point, KNN and range queries. Using data-dependent reference points, the ML …
Are updatable learned indexes ready?
Recently, numerous promising results have shown that updatable learned indexes can
perform better than traditional indexes with much lower memory space consumption. But it is …
perform better than traditional indexes with much lower memory space consumption. But it is …