22 citations · 41 across the 9 of their papers we have counts for
9 papers
Conformal Prediction for Verifiable Learned Query Optimization
Hanwen Liu, Shashank Giridhara, Ibrahim Sabek
Query optimization is critical in relational databases. Recently, numerous Learned Query Optimizers (LQOs) have been proposed, demonstrating superior performance over traditional h…
Enhancing In-Memory Spatial Indexing with Learned Search
Varun Pandey, Alexander van Renen, Eleni Tzirita Zacharatou +5
Spatial data is ubiquitous. Massive amounts of data are generated every day from a plethora of sources such as billions of GPS-enabled devices (e.g., cell phones, cars, and sensors…
The Case for Learned In-Memory Joins
Ibrahim Sabek, Tim Kraska
In-memory join is an essential operator in any database engine. It has been extensively investigated in the database literature. In this paper, we study whether exploiting the CDF-…
When Are Learned Models Better Than Hash Functions?
Ibrahim Sabek, Kapil Vaidya, Dominik Horn +2
In this work, we aim to study when learned models are better hash functions, particular for hash-maps. We use lightweight piece-wise linear models to replace the hash functions as…
The Case for Distance-Bounded Spatial Approximations
Eleni Tzirita Zacharatou, Andreas Kipf, Ibrahim Sabek +3
Spatial approximations have been traditionally used in spatial databases to accelerate the processing of complex geometric operations. However, approximations are typically only us…
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…