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https://github.com/boniolp/dsymb-playground
[ICDE 2024] Python and Streamlit implementation of "d_{symb} playground: an interactive tool to explore large multivariate time series datasets"
https://github.com/boniolp/dsymb-playground
clustering data-science data-visualization streamlit symbolization time-series time-series-analysis webapp
Last synced: about 2 months ago
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[ICDE 2024] Python and Streamlit implementation of "d_{symb} playground: an interactive tool to explore large multivariate time series datasets"
- Host: GitHub
- URL: https://github.com/boniolp/dsymb-playground
- Owner: boniolp
- License: mit
- Created: 2023-10-10T13:00:34.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-10T18:32:06.000Z (10 months ago)
- Last Synced: 2024-03-10T19:38:17.888Z (10 months ago)
- Topics: clustering, data-science, data-visualization, streamlit, symbolization, time-series, time-series-analysis, webapp
- Language: Python
- Homepage: https://dsymb-playground.streamlit.app/
- Size: 10.2 MB
- Stars: 7
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
d_{symb} playground
A fast interactive exploration of multivariate time series datasets
$d_{symb}$ playground is a Python-based web interactive tool to interpet and
compare large multivariate time series datasets.
It is based on a novel symbolic representation, called $d_{symb}$, for
multivariate time series.
$d_{symb}$ allows to visualize a dataset of multivariate time series with
a single glance, thus to quickly gain insights on your data.
$d_{symb}$ also comes with a compatible distance measure to compare the
obtained symbolic sequences.
Apart from its relevance on data mining tasks, this distance measure is also
fast.
Indeed, comparing a dataset of 80 time series (with 80 dimensions
and 5,000 timestamps) requires 20 seconds instead of 2,000 seconds for DTW-based
analysis.
### Reference
This repository contains the code that supports the following publication on the $d_{symb}$ playground.
Demo paper of the $d_{symb}$ playground [[paper](https://icde2024.github.io/demos.html) / [PDF](http://www.laurentoudre.fr/publis/dsymb_demo.pdf) / [Streamlit app](https://dsymb-playground.streamlit.app/) / [4 min YouTube video](https://youtu.be/4verma-Aqo8)]:
> S. W. Combettes, P. Boniol, C. Truong, and L. Oudre. d_{symb} playground: an interactive tool to explore large multivariate time series datasets. In _Proceedings of the International Conference on Data Engineering (ICDE)_ (to appear), Utrecht, Netherlands, 2024.
```bibtex
@inproceedings{2024_combettes_dsymb_playground_icde,
title={d_{symb} playground: an interactive tool to explore large multivariate time series datasets},
author={Sylvain W. Combettes and Paul Boniol and Charles Truong and Laurent Oudre},
booktitle={Proceedings of the International Conference on Data Engineering (ICDE) (to appear)},
year={2024},
location={Utrecht, Netherlands},
}
```
Method paper of $d_{symb}$ [[paper](https://ieeexplore.ieee.org/abstract/document/10411636) / [PDF](http://www.laurentoudre.fr/publis/ICDM2023.pdf) / [code](https://github.com/sylvaincom/d-symb)]:
> S. W. Combettes, C. Truong, and L. Oudre. An Interpretable Distance Measure for Multivariate Non-Stationary Physiological Signals. In _Proceedings of the International Conference on Data Mining Workshops (ICDMW)_, Shanghai, China, 2023.
```bibtex
@inproceedings{2023_combettes_dsymb_icdm,
author={Combettes, Sylvain W. and Truong, Charles and Oudre, Laurent},
booktitle={2023 IEEE International Conference on Data Mining Workshops (ICDMW)},
title={An Interpretable Distance Measure for Multivariate Non-Stationary Physiological Signals},
year={2023},
pages={533-539},
doi={10.1109/ICDMW60847.2023.00076},
location={Shanghai, China},
}
```
## Contributors
* [Sylvain W. Combettes](https://sylvaincom.github.io/) (Centre Borelli, ENS Paris-Saclay)
* [Paul Boniol](https://boniolp.github.io/) (Inria, ENS, DIENS, PSL, CNRS)
* [Charles Truong](https://charles.doffy.net/) (Centre Borelli, ENS Paris-Saclay)
* [Laurent Oudre](http://www.laurentoudre.fr/) (Centre Borelli, ENS Paris-Saclay)
## Usage
**Step 1:** Clone this repository using `git` and change into its root directory.
```bash
git clone https://github.com/boniolp/dsymb-playground.git
cd dsymb-playground/
```
**Step 2:** Create and activate a `conda` environment and install the dependencies.
```bash
conda create -n dsymb-playground python=3.9
conda activate dsymb-playground
pip install -r requirements.txt
```
**Step 3:** You can use our tool in two different ways:
- Access online: https://dsymb-playground.streamlit.app/
- Run locally (preferable for large time series datasets). To do so, run the following command:
```bash
streamlit run app.py
```
You can then open the app using your web browser. You can upload any kind of time series (one file per time series) with the shape `(n_timestamps, n_dims)`.
A preprocessed version of the dataset [JIGSAWS dataset](https://cirl.lcsr.jhu.edu/research/hmm/datasets/jigsaws_release/) can be found [here](https://kiwi.cmla.ens-cachan.fr/index.php/s/ctEdTsz6sxPBxxX).
## Acknowledgments
Sylvain W. Combettes is supported by the IDAML chair (ENS Paris-Saclay) and UDOPIA (ANR-20-THIA-0013-01).
Charles Truong is funded by the PhLAMES chair (ENS Paris-Saclay).
Part of the computations has been executed on Atos Edge computer, funded by the IDAML chair (ENS Paris-Saclay).
