TrainDB’s Main R&D Topics

# TrainDB: an ML-model based approximate query processing engine

  • SQL-like approximate query language

  • Approximate query processing using synopsis data that are synthesized by ML models

  • Approximate query processing using ML inference models

  • Various DBMS data sources support via extensible data source adapters

# ML model library for approximate query processing

  • Synopsis generative ML models + inferential ML models * Synopsis generative ML models: GAN-based models(e.g., TableGAN, OCT-GAN), score-based generative models * Inferential ML models: mixture density networks(MDN), relational sum-product networks(RSPN)

  • Error estimation for approximate query evaluation

  • Continual learning to update base table changes

# Cloud ML model serving framework

  • A framework for training/serving ML models in remote GPU servers

  • Kubeflow-based ML model registry/training/serving support

# Visual Exploratory Data Analysis Support Tools for TrainDB

  • Approximation query result visualization for exploratory data analysis

  • Visual OLAP analysis support for multi-dimensional data analysis