PTRAIL: A Mobility-data Preprocessing Library using parallel computation.
Project description
PTRAIL: A Parallel TRajectory dAta preprocessIng Library
Introduction
PTRAIL is a state-of-the art Mobility Data Preprocessing Library that mainly deals with filtering data, generating features and interpolation of Trajectory Data.
The main features of PTRAIL are:
- PTRAIL uses primarily parallel computation based on python Pandas and numpy which makes it very fast as compared to other libraries available.
- PTRAIL harnesses the full power of the machine that it is running on by using all the cores available in the computer.
- PTRAIL uses a customized DataFrame built on top of python pandas for representation and storage of Trajectory Data.
- PTRAIL also provides several Temporal and spatial features which are calculated mostly using parallel computation for very fast and accurate calculations.
- Moreover, PTRAIL also provides several filteration and outlier detection methods for cleaning and noise reduction of the Trajectory Data.
- Apart from the features mentioned above, four different kinds of Trajectory Interpolation techniques are offered by PTRAIL which is a first in the community.
Documentation
Pip Installation
pip install PTRAIL
Examples
Miscellaneous
Citation
To cite PTRAIL in your academic work, please use the following citation:
@article{haidri2021ptrail,
title={PTRAIL -- A python package for parallel trajectory data preprocessing},
author={Salman Haidri and Yaksh J. Haranwala and Vania Bogorny and Chiara Renso and Vinicius Prado da Fonseca and Amilcar Soares},
year={2021},
eprint={2108.13202},
url={https://arxiv.org/abs/2108.13202},
archivePrefix={arXiv},
primaryClass={cs.DC}
}
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