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Found 7 papers, 0 papers with code
JoinBoost: Grow Trees Over Normalized Data Using Only SQL
Although dominant for tabular data, ML libraries that train tree models over normalized databases (e. g., LightGBM, XGBoost) require the data to be denormalized as a single table, materialized, and exported.
End-to-end Optimization of Machine Learning Prediction Queries
First, it employs logical optimizations that pass information between the data part (and the properties of the underlying data) and the ML part to optimize each other.
Query Processing on Tensor Computation Runtimes
Finally, TQP can accelerate queries mixing ML predictions and SQL end-to-end, and deliver up to 9$\times$ speedup over CPU baselines.
Predictive Price-Performance Optimization for Serverless Query Processing
We present an efficient, parametric modeling framework for predictive resource allocations, focusing on the amount of computational resources, that can optimize for a range of price-performance objectives for data analytics in serverless query processing settings.
Optimal Resource Allocation for Serverless Queries
We introduce a system for optimal resource allocation that can predict performance with aggressive trade-offs, for both new and past observed queries.
Extending Relational Query Processing with ML Inference
The broadening adoption of machine learning in the enterprise is increasing the pressure for strict governance and cost-effective performance, in particular for the common and consequential steps of model storage and inference.
Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
Consequently, rigorous data management has emerged as a key requirement in enterprise settings.
