4 citations · 7 across the 2 of their papers we have counts for
5 papers · 1 filter
Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction
Benjamin Hilprecht, Carsten Binnig
In this paper, we introduce zero-shot cost models which enable learned cost estimation that generalizes to unseen databases. In contrast to state-of-the-art workload-driven approac…
ReStore -- Neural Data Completion for Relational Databases
Benjamin Hilprecht, Carsten Binnig
Classical approaches for OLAP assume that the data of all tables is complete. However, in case of incomplete tables with missing tuples, classical approaches fail since the result…
DeepDB: Learn from Data, not from Queries!
Benjamin Hilprecht, Andreas Schmidt, Moritz Kulessa +3
The typical approach for learned DBMS components is to capture the behavior by running a representative set of queries and use the observations to train a machine learning model. T…
Learning a Partitioning Advisor with Deep Reinforcement Learning
Benjamin Hilprecht, Carsten Binnig, Uwe Roehm
Commercial data analytics products such as Microsoft Azure SQL Data Warehouse or Amazon Redshift provide ready-to-use scale-out database solutions for OLAP-style workloads in the c…
Model-based Approximate Query Processing
Moritz Kulessa, Alejandro Molina, Carsten Binnig +2
Interactive visualizations are arguably the most important tool to explore, understand and convey facts about data. In the past years, the database community has been working on di…