Search Results for author: Jialin Ding

Found 6 papers, 0 papers with code

Cortex: Harnessing Correlations to Boost Query Performance

no code implementations12 Dec 2020 Vikram Nathan, Jialin Ding, Tim Kraska, Mohammad Alizadeh

Unlike prior work, Cortex can adapt itself to any existing primary index, whether single or multi-dimensional, to harness a broad variety of correlations, such as those that exist between more than two attributes or have a large number of outliers.

Attribute

The Case for Learned Spatial Indexes

no code implementations24 Aug 2020 Varun Pandey, Alexander van Renen, Andreas Kipf, Ibrahim Sabek, Jialin Ding, Alfons Kemper

This exponential growth in spatial data has led the research community to focus on building systems and applications that can process spatial data efficiently.

Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads

no code implementations23 Jun 2020 Jialin Ding, Vikram Nathan, Mohammad Alizadeh, Tim Kraska

Filtering data based on predicates is one of the most fundamental operations for any modern data warehouse.

Learning Multi-dimensional Indexes

no code implementations3 Dec 2019 Vikram Nathan, Jialin Ding, Mohammad Alizadeh, Tim Kraska

Scanning and filtering over multi-dimensional tables are key operations in modern analytical database engines.

LISA: Towards Learned DNA Sequence Search

no code implementations10 Oct 2019 Darryl Ho, Jialin Ding, Sanchit Misra, Nesime Tatbul, Vikram Nathan, Vasimuddin Md, Tim Kraska

Next-generation sequencing (NGS) technologies have enabled affordable sequencing of billions of short DNA fragments at high throughput, paving the way for population-scale genomics.

ALEX: An Updatable Adaptive Learned Index

no code implementations21 May 2019 Jialin Ding, Umar Farooq Minhas, JIA YU, Chi Wang, Jaeyoung Do, Yi-Nan Li, Hantian Zhang, Badrish Chandramouli, Johannes Gehrke, Donald Kossmann, David Lomet, Tim Kraska

The original work by Kraska et al. shows that a learned index beats a B+Tree by a factor of up to three in search time and by an order of magnitude in memory footprint.

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