# GreyCat: Efficient What-If Analytics for Data in Motion at Scale

arXiv: https://arxiv.org/abs/1803.09627 · HTML: https://mlsanity.com/paper/1803.09627/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2018.

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

- [A Model-Driven Approach to Machine Learning and Software Modeling for the IoT](https://mlsanity.com/paper/2107.02689/) (2021)
- [Supporting AI Engineering on the IoT Edge through Model-Driven TinyML](https://mlsanity.com/paper/2107.02690/) (2021)
- [Model-Driven Quantum Federated Learning (QFL)](https://mlsanity.com/paper/2304.08496/) (2023)
- [MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna & ML-Quadrat](https://mlsanity.com/paper/2209.07282/) (2022)

## Abstract (arXiv)

Over the last few years, data analytics shifted from a descriptive era, confined to the explanation of past events, to the emergence of predictive techniques. Nonetheless, existing predictive techniques still fail to effectively explore alternative futures, which continuously diverge from current situations when exploring the effects of what-if decisions. Enabling prescriptive analytics therefore calls for the design of scalable systems that can cope with the complexity and the diversity of underlying data models. In this article, we address this challenge by combining graphs and time series within a scalable storage system that can organize a massive amount of unstructured and continuously changing data into multi-dimensional data models, called Many-Worlds Graphs. We demonstrate that our open source implementation, GreyCat, can efficiently fork and update thousands of parallel worlds composed of millions of timestamped nodes, such as what-if exploration.