# Simple-ML: Towards a Framework for Semantic Data Analytics Workflows

arXiv: https://arxiv.org/abs/1909.05859 · HTML: https://mlsanity.com/paper/1909.05859/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Achieving Guidance in Applied Machine Learning through Software Engineering Techniques](https://mlsanity.com/paper/2203.15510/) (2022)
- [Tab2KG: Semantic Table Interpretation with Lightweight Semantic Profiles](https://mlsanity.com/paper/2302.01150/) (2023)
- [Improving the Learnability of Machine Learning APIs by Semi-Automated API Wrapping](https://mlsanity.com/paper/2203.15491/) (2022)

## Abstract (arXiv)

In this paper we present the Simple-ML framework that we develop to support efficient configuration, robustness and reusability of data analytics workflows through the adoption of semantic technologies. We present semantic data models that lay the foundation for the framework development and discuss the data analytics workflows based on these models. Furthermore, we present an example instantiation of the Simple-ML data models for a real-world use case in the mobility domain.