On the recordRead 8 Oct 202619 facts
PluRel
On the record
- Compatibility
- Generated datasets can be made compatible with RelBench.star-project.stanford.edu · read 4 Oct 2026
- Configurable output
- Its configuration controls table layouts, table and row counts, column counts, and structural causal model parameters.star-project.stanford.edu · read 8 Oct 2026
- Data types and patterns
- Feature generation supports numeric, categorical, and boolean values, with temporal trends, cycles, and fluctuations.star-project.stanford.edu · read 4 Oct 2026
- Feature types
- Generated feature columns support numeric, categorical, and boolean types.star-project.stanford.edu · read 8 Oct 2026
- Generation stages
- It models database schemas with directed graphs, inter-table primary–foreign key links with bipartite graphs, and feature distributions with conditional causal mechanisms.star-project.stanford.edu · read 8 Oct 2026
- Installation
- The library is installed with pip install plurel and requires Python 3.12 or later.github.com · read 8 Oct 2026
- Installation requirement
- The library is installed with pip and requires Python 3.12 or later.github.com · read 4 Oct 2026
- Intended context
- The project targets relational foundation model research and data-driven work with complex multi-table databases.star-project.stanford.edu · read 4 Oct 2026
- Intended use
- The project describes PluRel as a framework for research on relational foundation models and synthetic relational database generation.arxiv.org · read 8 Oct 2026
- Large-scale generation
- A multiprocessing script can generate databases in parallel, with the number of databases and processes configurable.github.com · read 4 Oct 2026
- License
- The GitHub repository identifies the project license as MIT.github.com · read 8 Oct 2026
- Parallel generation
- A multiprocessing-based script can generate databases in parallel, with the process count configurable.github.com · read 8 Oct 2026
- Purpose
- PluRel is an open-source library for synthesizing relational and tabular data.github.com · read 8 Oct 2026
- RelBench compatibility
- The project says its generated dataset objects are compatible with RelBench.github.com · read 8 Oct 2026
- Research results
- The project reports that synthetic database diversity and pretraining token count both show power-law scaling in relational foundation model pretraining loss.star-project.stanford.edu · read 8 Oct 2026
- Schema input
- It can generate data from an existing SQL schema using the optional schema_file setting.star-project.stanford.edu · read 4 Oct 2026
- SQL schemas
- The configuration can optionally use an existing SQL schema as input.star-project.stanford.edu · read 8 Oct 2026
- Temporal patterns
- Feature generation can model temporal correlations using trend, cycle, and fluctuation components.star-project.stanford.edu · read 8 Oct 2026
