# A Survey on Domain-Specific Languages for Machine Learning in Big Data

arXiv: https://arxiv.org/abs/1602.07637 · HTML: https://mlsanity.com/paper/1602.07637/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2016.

## Built on by (most cited first, 4 of 4)

- [Model-driven Engineering for Machine Learning Components: A Systematic Literature Review](https://mlsanity.com/paper/2311.00284/) (2023)
- [Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures](https://mlsanity.com/paper/2304.12576/) (2023)
- [Bridging MDE and AI: A Systematic Review of Domain-Specific Languages and Model-Driven Practices in AI Software Systems Engineering](https://mlsanity.com/paper/2307.04599/) (2023)
- [Energy and Thermal-aware Resource Management of Cloud Data Centres: A Taxonomy and Future Directions](https://mlsanity.com/paper/2107.02342/) (2021)

## Abstract (arXiv)

The amount of data generated in the modern society is increasing rapidly. New problems and novel approaches of data capture, storage, analysis and visualization are responsible for the emergence of the Big Data research field. Machine Learning algorithms can be used in Big Data to make better and more accurate inferences. However, because of the challenges Big Data imposes, these algorithms need to be adapted and optimized to specific applications. One important decision made by software engineers is the choice of the language that is used in the implementation of these algorithms. Therefore, this literature survey identifies and describes domain-specific languages and frameworks used for Machine Learning in Big Data. By doing this, software engineers can then make more informed choices and beginners have an overview of the main languages used in this domain.