# Powering In-Database Dynamic Model Slicing for Structured Data Analytics

arXiv: https://arxiv.org/abs/2405.00568 · HTML: https://mlsanity.com/paper/2405.00568/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in MoE large language models, one of 12 directions in Mixture-of-experts.
- Builds on 30 of its 76 references that are in the map.
- First posted on arXiv 1 May 2024.

## Placed in

- Mixture-of-experts / [MoE large language models](https://mlsanity.com/moe/moe-large-language-models/)

## Builds on (most cited first, 30 of 30)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://mlsanity.com/paper/2101.03961/) (2021)
- [GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding](https://mlsanity.com/paper/2006.16668/) (2020)
- [Scaling Vision with Sparse Mixture of Experts](https://mlsanity.com/paper/2106.05974/) (2021)
- [Learning Efficient Convolutional Networks through Network Slimming](https://mlsanity.com/paper/1708.06519/) (2017)
- [Mixture-of-Experts with Expert Choice Routing](https://mlsanity.com/paper/2202.09368/) (2022)
- [TabTransformer: Tabular Data Modeling Using Contextual Embeddings](https://mlsanity.com/paper/2012.06678/) (2020)
- [From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification](https://mlsanity.com/paper/1602.02068/) (2016)
- [MoE-LLaVA: Mixture of Experts for Large Vision-Language Models](https://mlsanity.com/paper/2401.15947/) (2024)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [Learning Factored Representations in a Deep Mixture of Experts](https://mlsanity.com/paper/1312.4314/) (2013)
- [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://mlsanity.com/paper/1803.05170/) (2018)
- [Continual learning with hypernetworks](https://mlsanity.com/paper/1906.00695/) (2019)
- [From Sparse to Soft Mixtures of Experts](https://mlsanity.com/paper/2308.00951/) (2023)
- [Clipper: A Low-Latency Online Prediction Serving System](https://mlsanity.com/paper/1612.03079/) (2016)
- [Neural Factorization Machines for Sparse Predictive Analytics](https://mlsanity.com/paper/1708.05027/) (2017)
- [Sparse Sequence-to-Sequence Models](https://mlsanity.com/paper/1905.05702/) (2019)
- [FastMoE: A Fast Mixture-of-Expert Training System](https://mlsanity.com/paper/2103.13262/) (2021)
- [Synthesizing Tabular Data using Generative Adversarial Networks](https://mlsanity.com/paper/1811.11264/) (2018)
- [Go Wider Instead of Deeper](https://mlsanity.com/paper/2107.11817/) (2021)
- [Adaptive Factorization Network: Learning Adaptive-Order Feature Interactions](https://mlsanity.com/paper/1909.03276/) (2019)
- [Rafiki: Machine Learning as an Analytics Service System](https://mlsanity.com/paper/1804.06087/) (2018)
- [End-to-end Optimization of Machine Learning Prediction Queries](https://mlsanity.com/paper/2206.00136/) (2022)
- [TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications](https://mlsanity.com/paper/2003.12012/) (2020)
- [Optimizing Machine Learning Inference Queries with Correlative Proxy Models](https://mlsanity.com/paper/2201.00309/) (2022)
- [Learning Models over Relational Data using Sparse Tensors and Functional Dependencies](https://mlsanity.com/paper/1703.04780/) (2017)
- [Model Slicing for Supporting Complex Analytics with Elastic Inference Cost and Resource Constraints](https://mlsanity.com/paper/1904.01831/) (2019)
- [Optimal binning: mathematical programming formulation](https://mlsanity.com/paper/2001.08025/) (2020)
- [F-IVM: Learning over Fast-Evolving Relational Data](https://mlsanity.com/paper/2006.00694/) (2020)

## Built on by (most cited first, 5 of 5)

- [NeurDB: An AI-powered Autonomous Data System](https://mlsanity.com/paper/2405.03924/) (2024)
- [NeurStore: Efficient In-database Deep Learning Model Management System](https://mlsanity.com/paper/2509.03228/) (2025)
- [SliceGX: Layer-wise GNN Explanation with Model-slicing](https://mlsanity.com/paper/2506.17977/) (2025)
- [Aixel: A Unified, Adaptive and Extensible System for AI-powered Data Analysis](https://mlsanity.com/paper/2510.12642/) (2025)
- [Towards Effective Orchestration of AI x DB Workloads](https://mlsanity.com/paper/2603.03772/) (2026)

## Abstract (arXiv)

Relational database management systems (RDBMS) are widely used for the storage of structured data. To derive insights beyond statistical aggregation, we typically have to extract specific subdatasets from the database using conventional database operations, and then apply deep neural networks (DNN) training and inference on these subdatasets in a separate analytics system. The process can be prohibitively expensive, especially when there are various subdatasets extracted for different analytical purposes. This calls for efficient in-database support of advanced analytical methods. In this paper, we introduce LEADS, a novel SQL-aware dynamic model slicing technique to customize models for specified SQL queries. LEADS improves the predictive modeling of structured data via the mixture of experts (MoE) and maintains efficiency by a SQL-aware gating network. At the core of LEADS is the construction of a general model with multiple expert sub-models trained over the database. The MoE scales up the modeling capacity, enhances effectiveness, and preserves efficiency by activating necessary experts via the SQL-aware gating network during inference. To support in-database analytics, we build an inference extension that integrates LEADS onto PostgreSQL. Our extensive experiments on real-world datasets demonstrate that LEADS consistently outperforms the baseline models, and the in-database inference extension delivers a considerable reduction in inference latency compared to traditional solutions.