19 citations · 37 across the 4 of their papers we have counts for
7 papers
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…
The Case for Learned Spatial Indexes
Varun Pandey, Alexander van Renen, Andreas Kipf +3
Spatial data is ubiquitous. Massive amounts of data are generated every day from billions of GPS-enabled devices such as cell phones, cars, sensors, and various consumer-based appl…
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…
ALEX: An Updatable Adaptive Learned Index
Jialin Ding, Umar Farooq Minhas, Jia Yu +9
Recent work on "learned indexes" has changed the way we look at the decades-old field of DBMS indexing. The key idea is that indexes can be thought of as "models" that predict the…