automatise 0.1b21

Automatise: A Multiple Aspect Trajectory Data Mining Tool Library

Automatise: Multiple Aspect Trajectory Data Mining Tool Library

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[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.

Bibtex:

@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.