{"id":19109475,"url":"https://github.com/boniolp/dsymb-playground","last_synced_at":"2025-04-30T20:06:52.540Z","repository":{"id":199495878,"uuid":"703023824","full_name":"boniolp/dsymb-playground","owner":"boniolp","description":"[ICDE 2024] Python and Streamlit implementation of \"d_{symb} playground: an interactive tool to explore large multivariate time series 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align=\"center\"\u003e\n\u003cimg width=\"230\" src=\"./figures/Symbol_logo.png\"/\u003e\n\u003c/p\u003e\n\n\u003ch1 align=\"center\"\u003ed_{symb} playground\u003c/h1\u003e\n\u003ch2 align=\"center\"\u003eA fast interactive exploration of multivariate time series datasets\u003c/h2\u003e\n\n\u003cdiv align=\"center\"\u003e\n\u003cp\u003e\n\u003cimg alt=\"GitHub\" src=\"https://img.shields.io/github/license/boniolp/dsymb-playground\"\u003e \u003cimg alt=\"GitHub issues\" src=\"https://img.shields.io/github/issues/boniolp/dsymb-playground\"\u003e\n\u003c/p\u003e\n\u003c/div\u003e\n\n\u003cp align=\"center\"\u003e\u003ca href=\"https://dsymb-playground.streamlit.app/\"\u003eTry our demo\u003c/a\u003e\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n\u003cimg width=\"500\" src=\"./figures/demo_capture.gif\"/\u003e\n\u003c/p\u003e\n\n$d_{symb}$ playground is a Python-based web interactive tool to interpet and\ncompare large multivariate time series datasets.\nIt is based on a novel symbolic representation, called $d_{symb}$, for\nmultivariate time series.\n$d_{symb}$ allows to visualize a dataset of multivariate time series with\na single glance, thus to quickly gain insights on your data.\n$d_{symb}$ also comes with a compatible distance measure to compare the\nobtained symbolic sequences.\nApart from its relevance on data mining tasks, this distance measure is also\nfast.\nIndeed, comparing a dataset of 80 time series (with 80 dimensions\nand 5,000 timestamps) requires 20 seconds instead of 2,000 seconds for DTW-based\nanalysis.\n\n### Reference\n\nThis repository contains the code that supports the following publication on the $d_{symb}$ playground.\n\nDemo 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)]:\n\u003e 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.\n\n```bibtex\n@inproceedings{2024_combettes_dsymb_playground_icde,\n  title={d_{symb} playground: an interactive tool to explore large multivariate time series datasets},\n  author={Sylvain W. Combettes and Paul Boniol and Charles Truong and Laurent Oudre},\n  booktitle={Proceedings of the International Conference on Data Engineering (ICDE) (to appear)},\n  year={2024},\n  location={Utrecht, Netherlands},\n}\n```\n\nMethod 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)]:\n\u003e 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.\n\n```bibtex\n@inproceedings{2023_combettes_dsymb_icdm,\n  author={Combettes, Sylvain W. and Truong, Charles and Oudre, Laurent},\n  booktitle={2023 IEEE International Conference on Data Mining Workshops (ICDMW)}, \n  title={An Interpretable Distance Measure for Multivariate Non-Stationary Physiological Signals}, \n  year={2023},\n  pages={533-539},\n  doi={10.1109/ICDMW60847.2023.00076},\n  location={Shanghai, China},\n}\n```\n\n## Contributors\n\n* [Sylvain W. Combettes](https://sylvaincom.github.io/) (Centre Borelli, ENS Paris-Saclay)\n* [Paul Boniol](https://boniolp.github.io/) (Inria, ENS, DIENS, PSL, CNRS)\n* [Charles Truong](https://charles.doffy.net/) (Centre Borelli, ENS Paris-Saclay)\n* [Laurent Oudre](http://www.laurentoudre.fr/) (Centre Borelli, ENS Paris-Saclay)\n\n## Usage\n\n**Step 1:** Clone this repository using `git` and change into its root directory.\n\n```bash\ngit clone https://github.com/boniolp/dsymb-playground.git\ncd dsymb-playground/\n```\n\n**Step 2:** Create and activate a `conda` environment and install the dependencies.\n\n```bash\nconda create -n dsymb-playground python=3.9\nconda activate dsymb-playground\npip install -r requirements.txt\n```\n\n**Step 3:** You can use our tool in two different ways: \n\n- Access online: https://dsymb-playground.streamlit.app/\n- Run locally (preferable for large time series datasets). To do so, run the following command:\n\n```bash\nstreamlit run app.py\n```\n\nYou 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)`.\nA 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).\n\n## Acknowledgments\n\nSylvain W. Combettes is supported by the IDAML chair (ENS Paris-Saclay) and UDOPIA (ANR-20-THIA-0013-01).\nCharles Truong is funded by the PhLAMES chair (ENS Paris-Saclay).\nPart of the computations has been executed on Atos Edge computer, funded by the IDAML chair (ENS Paris-Saclay).\n\n\u003cp align=\"center\"\u003e\n\u003cimg width=\"700\" src=\"./figures/cebo_logos.png\"/\u003e\n\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fboniolp%2Fdsymb-playground","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fboniolp%2Fdsymb-playground","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fboniolp%2Fdsymb-playground/lists"}