# DeepMapping: Learned Data Mapping for Lossless Compression and Efficient Lookup

arXiv: https://arxiv.org/abs/2307.05861 · HTML: https://mlsanity.com/paper/2307.05861/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2023.

## Built on by (most cited first, 2 of 2)

- [Learned Static Function Data Structures](https://mlsanity.com/paper/2510.27588/) (2025)
- [InferF: Declarative Factorization of AI/ML Inferences over Joins](https://mlsanity.com/paper/2511.20489/) (2025)

## Abstract (arXiv)

Storing tabular data to balance storage and query efficiency is a long-standing research question in the database community. In this work, we argue and show that a novel DeepMapping abstraction, which relies on the impressive memorization capabilities of deep neural networks, can provide better storage cost, better latency, and better run-time memory footprint, all at the same time. Such unique properties may benefit a broad class of use cases in capacity-limited devices. Our proposed DeepMapping abstraction transforms a dataset into multiple key-value mappings and constructs a multi-tasking neural network model that outputs the corresponding values for a given input key. To deal with memorization errors, DeepMapping couples the learned neural network with a lightweight auxiliary data structure capable of correcting mistakes. The auxiliary structure design further enables DeepMapping to efficiently deal with insertions, deletions, and updates even without retraining the mapping. We propose a multi-task search strategy for selecting the hybrid DeepMapping structures (including model architecture and auxiliary structure) with a desirable trade-off among memorization capacity, size, and efficiency. Extensive experiments with a real-world dataset, synthetic and benchmark datasets, including TPC-H and TPC-DS, demonstrated that the DeepMapping approach can better balance the retrieving speed and compression ratio against several cutting-edge competitors.