English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Innovative learning based structures have recently been proposed to tackleindex and cardinality estimation tasks, specifically learned indexes and datadriven cardinality estimators. These structures exhibit excellent performancein capturing data distribution, making them promising for integration into AIdriven database kernels. However, accurate estimation for corner case queriesrequires a large number of network parameters, resulting in higher computingresources on expensive GPUs and more storage overhead. Additionally, theseparate implementation for CE and learned index result in a redundancy wasteby storage of single table distribution twice. These present challenges fordesigning AI driven database kernels. As in real database scenarios, a compactkernel is necessary to process queries within a limited storage and timebudget. Directly integrating these two AI approaches would result in a heavyand complex kernel due to a large number of network parameters and repeatedstorage of data distribution parameters. Our proposed CardIndex structureeffectively killed two birds with one stone. It is a fast multidim learnedindex that also serves as a lightweight cardinality estimator with parametersscaled at the KB level. Due to its special structure and small parameter size,it can obtain both CDF and PDF information for tuples with an incredibly lowlatency of 1 to 10 microseconds. For tasks with low selectivity estimation, wedid not increase the model's parameters to obtain fine grained point density.Instead, we fully utilized our structure's characteristics and proposed ahybrid estimation algorithm in providing fast and exact results.

One stone, two birds: A lightweight multidimensional learned index with cardinality support