https://github.com/maxmouchet/atlas-trends-demo

Demonstration of the RIPE Atlas Trends API for RTT time series clustering.
https://github.com/maxmouchet/atlas-trends-demo

api clustering ripe-atlas rtt time-series

Last synced: about 1 month ago
JSON representation

Demonstration of the RIPE Atlas Trends API for RTT time series clustering.

• Host: GitHub
• URL: https://github.com/maxmouchet/atlas-trends-demo
• Owner: maxmouchet
• License: mit
• Created: 2019-04-16T15:49:24.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-12-08T03:24:21.000Z (about 2 years ago)
• Last Synced: 2024-12-01T12:47:18.600Z (about 1 month ago)
• Topics: api, clustering, ripe-atlas, rtt, time-series
• Language: Jupyter Notebook
• Homepage:
• Size: 163 KB
• Stars: 12
• Watchers: 7
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Atlas Trends API demonstration

![Example Segmentation](segmentation.png)

## Introduction

The Atlas Trends API is an implementation of a novel method to cluster RTT time series using nonparametric Bayesian models. The API allows producing humanlike segmentation of [RIPE Atlas](http://atlas.ripe.net/) RTT time series.

This repository contains the following Python notebooks demonstrating the API usage:

Name | Description | Online Notebook

:----|:------------|:-----------------

Atlas Trends API | Overview of the API | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/maxmouchet/atlas-trends-demo/blob/master/notebooks/Atlas%20Trends%20API.ipynb)

## Citation

M. Mouchet, S. Vaton, T. Chonavel, E. Aben and J. D. Hertog, "Large-Scale Characterization and Segmentation of Internet Path Delays With Infinite HMMs," in [_IEEE Access_](https://ieeexplore.ieee.org/document/8964300), vol. 8, pp. 16771-16784, 2020.

```bibtex

@article{mouchet2019large,

  author={M. {Mouchet} and S. {Vaton} and T. {Chonavel} and E. {Aben} and J. {Den Hertog}},

  journal={IEEE Access},

  title={Large-Scale Characterization and Segmentation of Internet Path Delays With Infinite HMMs},

  year={2020},

  volume={8},

  pages={16771-16784},

  doi={10.1109/ACCESS.2020.2968380},

  ISSN={2169-3536}

}

```

## Getting Started

You can run the notebooks on Google Colab by following the links at the top, or locally by running the following in a terminal:

```bash

git clone https://github.com/maxmouchet/atlas-trends-demo.git

cd atlas-trends-demo

python3 -m venv trends-env; source trends-env/bin/activate

pip install -r requirements.txt

jupyter lab

```