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arxiv 1511.03086 v3 pith:VL2ICX37 submitted 2015-11-10 cs.LG cs.DB

Jan Motl , Oliver Schulte This is my paper
classification cs.LGcs.DB
keywords learningnumberrelationalrepositorypragueservertablescolumns
verification ladder T0 review T1 audit T2 compute T3 formal
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The aim of the Prague Relational Learning Repository is to support machine learning research with multi-relational data. The repository currently contains 148 SQL databases hosted on a public MySQL server located at https://relational.fel.cvut.cz. The server is provided by the Czech Technical University (CTU). A searchable meta-database provides metadata (e.g., the number of tables in the database, the number of rows and columns in the tables, the number of self-relationships).

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Models

    cs.LG 2025-05 conditional novelty 7.0 of 10

    RelDiff synthesizes relational databases by generating their foreign-key graph with a stochastic block model and denoising all attributes jointly with a graph-conditioned diffusion model.

  2. Parameter-Free Encoders Remain Viable for RDB Foundation Models

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Trainable RDB encoders cannot robustly exploit neighborhood labels as fixed foundation-model features or feature-importance signals, so simple parameter-free encoders stay near-SOTA.

  3. No Need to Train Your RDB Foundation Model

    cs.AI 2026-02 conditional novelty 6.0 of 10

    Column-wise, parameter-free JUICE encodings let single-table ICL models solve multi-table RDB prediction tasks with no training or fine-tuning.

  4. Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases

    cs.DB 2025-08 unverdicted novelty 5.0 of 10

    SRP combines feature synthesis, cross-table retrieval, and graph propagation to improve predictive modeling on relational databases.

  5. Rel-HNN: Split Parallel Hypergraph Neural Network for Learning on Relational Databases

    cs.DB 2025-07 reject novelty 5.0 of 10

    Rel-HNN models each tuple as a hyperedge over attribute-value nodes and reports large accuracy gains, but its hypergraph construction appears to include the target label as an input node.

  6. From Features to Structure: Task-Aware Graph Construction for Relational and Tabular Learning with GNNs

    cs.LG 2025-06 conditional novelty 5.0 of 10

    Selective task-aware attribute promotion into graph nodes improves GNN classification on relational and tabular data compared to schema-based and heuristic graph construction.

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