178 citations · 212 across the 7 of their papers we have counts for
7 papers
Stage: Query Execution Time Prediction in Amazon Redshift
Ziniu Wu, Ryan Marcus, Zhengchun Liu +7
Query performance (e.g., execution time) prediction is a critical component of modern DBMSes. As a pioneering cloud data warehouse, Amazon Redshift relies on an accurate execution…
Cortex: Harnessing Correlations to Boost Query Performance
Vikram Nathan, Jialin Ding, Tim Kraska +1
Databases employ indexes to filter out irrelevant records, which reduces scan overhead and speeds up query execution. However, this optimization is only available to queries that f…
Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads
Jialin Ding, Vikram Nathan, Mohammad Alizadeh +1
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 clu…
Learning Multi-dimensional Indexes
Vikram Nathan, Jialin Ding, Mohammad Alizadeh +1
Scanning and filtering over multi-dimensional tables are key operations in modern analytical database engines. To optimize the performance of these operations, databases often crea…
LISA: Towards Learned DNA Sequence Search
Darryl Ho, Jialin Ding, Sanchit Misra +4
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…
Accurate Streaming Support Vector Machines
Vikram Nathan, Sharath Raghvendra
A widely-used tool for binary classification is the Support Vector Machine (SVM), a supervised learning technique that finds the "maximum margin" linear separator between the two c…