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Summary
This summary is machine-generated.

This study introduces an in-database inference design for decision forests, outperforming existing decoupled and in-database methods. The relation-centric approach shows superior performance for large machine learning models.

Keywords:
Decision ForestMachine Learning System

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Area of Science:

  • Machine Learning
  • Database Systems
  • Data Management

Background:

  • Decision forests (RandomForest, XGBoost, LightGBM) excel on tabular data.
  • Existing inference frameworks (ONNX, TreeLite, TF-DF, HummingBird, Nvidia FIL, lleaves) are decoupled from databases, causing performance overheads.

Purpose of the Study:

  • To develop and evaluate an in-database inference system for decision forests.
  • To analyze performance gaps between decoupled and in-database inference using a DICT model.
  • To compare UDF-centric and relation-centric in-database representations.

Main Methods:

  • Developed a DICT model to quantify performance differences.
  • Implemented and optimized both UDF-centric and relation-centric in-database inference approaches.
  • Conducted comprehensive benchmarks against decoupled frameworks and existing in-database solutions (SparkSQL, PostgresML).

Main Results:

  • The DICT model accurately reflects performance disparities.
  • The relation-centric in-database representation significantly outperforms the UDF-centric approach for large models.
  • The proposed in-database inference design demonstrates superior performance over all evaluated baselines.

Conclusions:

  • In-database inference for decision forests offers significant performance advantages.
  • The relation-centric representation is key to optimizing large model inference within databases.
  • This work bridges the gap between machine learning inference and efficient data management.