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source_url, ingested, sha256
| source_url | ingested | sha256 |
|---|---|---|
| https://arxiv.org/abs/2512.08483v3 | 2026-05-15 | 27f965211912f0ca663f249e72f2a2090294ca4538ada72e5dfc0ccf25f6523d |
NeurIDA: Dynamic Modeling for Effective In-Database Analytics
Authors: Lingze Zeng (NUS), Naili Xing (NUS), Shaofeng Cai (NUS), Peng Lu (ZJU), Gang Chen (ZJU), Jian Pei (Duke), Beng Chin Ooi (ZJU)
arXiv: 2512.08483v3 | Category: cs.DB | Date: 2025-12-15
Abstract
Relational Database Management Systems (RDBMS) manage complex, interrelated data and support a broad spectrum of analytical tasks. With the growing demand for predictive analytics, the deep integration of machine learning (ML) into RDBMS has become critical. However, a fundamental challenge hinders this evolution: conventional ML models are static and task-specific, whereas RDBMS environments are dynamic and must support diverse analytical queries. Each analytical task entails constructing a bespoke pipeline from scratch, which incurs significant development overhead and hence limits wide adoption of ML in analytics.
We present NeurIDA, an autonomous end-to-end system for in-database analytics that dynamically "tweaks" the best available base model to better serve a given analytical task. In particular, we propose a novel paradigm of dynamic in-database modeling to pre-train a composable base model architecture over the relational data. Upon receiving a task, NeurIDA formulates the task and data profile to dynamically select and configure relevant components from the pool of base models and shared model components for prediction. For friendly user experience, NeurIDA supports natural language queries; it interprets user intent to construct structured task profiles, and generates analytical reports with dedicated LLM agents. By design, NeurIDA enables ease-of-use and yet effective and efficient in-database AI analytics.
Results: Up to 12% improvement in AUC-ROC and 25% relative reduction in MAE across ten tasks on five real-world datasets.