Automatise: A Multiple Aspect Trajectory Data Mining Tool Library
Project description
Automatise: Multiple Aspect Trajectory Data Mining Tool Library
[Publication] [citation.bib] [GitHub] [PyPi]
Welcome to Automatise Framework for Multiple Aspect Trajectory Analysis. You can use it as a web-platform or a Python library.
The present application offers a tool, called AutoMATise, to support the user in the classification task of multiple aspect trajectories, specifically for extracting and visualizing the movelets, the parts of the trajectory that better discriminate a class. The AutoMATise integrates into a unique platform the fragmented approaches available for multiple aspects trajectories and in general for multidimensional sequence classification into a unique web-based and python library system. Offers both movelets visualization and a complete configuration of classification experimental settings.
Main Modules
- Datasets: Datasets descriptions, statistics and files to download;
- Methods: Methods for trajectory classification and movelet extraction;
- Scripting: Script generator for experimental evaluation on available methods (Linux shell);
- Results: Experiments on trajectory datasets and method rankings;
- Analysis: Multiple Aspect Trajectory Analysis Tool (trajectory and movelet visualization);
- Publications: Multiple Aspect Trajectory Analysis related publications;
- Tutorial: Tutorial on how to use Automatise as a Python library.
Installation
Install directly from PyPi repository, or, download from github. Intalling with pip will also provide command line scripts (available in folder automatise/scripts
). (python >= 3.5 required)
pip install automatise
To use Automatise as a python library, find examples in this sample Jupyter Notebbok: Automatise_Sample_Code.ipynb
Citing
If you use automatise
please cite the following paper:
Portela, Tarlis Tortelli; Bogorny, Vania; Bernasconi, Anna; Renso, Chiara. **AutoMATitse: Multiple Aspect Trajectory Data Mining Tool Library.** 2022. 23rd IEEE International Conference on Mobile Data Management (MDM), 2022, pp. xxx-xxx, doi: xxx.
@misc{Portela2022automatise,
title={},
author={},
year={2022},
}
Collaborate with us
Any contribution is welcome. This is an active project and if you would like to include your algorithm in automatise
, feel free to fork the project, open an issue and contact us.
Feel free to contribute in any form, such as scientific publications referencing automatise
, teaching material and workshop videos.
Related packages
- scikit-mobility: Human trajectory representation and visualizations in Python;
- geopandas: Library to help work with geospatial data in Python;
- movingpandas: Based on
geopandas
for movement data exploration and analysis.
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