7310526
doi
10.1109/MDM55031.2022.00060
oai:zenodo.org:7310526
user-master-h2020-msca-rise-project
user-eu
Vania Bogorny
Federal University of Santa Catarina
Anna Bernasconi
University of Pisa
Chiara Renso
ISTI-CNR
AUTOMATISE: Multiple Aspect Trajectory Data Mining Tool Library
Tarlis Tortelli Portela
Federal University of Santa Catarina
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
<p>ith the rapid increasing availability of information and popularization of mobility devices, trajectories have become more complex in their form. Trajectory data is now high dimensional, and often associated with heterogeneous sources of semantic data, that are called Multiple Aspect Trajectories. The high dimensionality and heterogeneity of these data makes classification a very challenging task both in term of accuracy and in terms of efficiency. The present demo 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 in the literature for multiple aspects trajectories and, in general, for multidimensional sequence classification into a unique web-based and python library system. We illustrate the architecture and the use of the tool for offering both movelets visualization and a complete configuration of classification experimental settings.</p>
Zenodo
2022-06-09
info:eu-repo/semantics/conferencePaper
7310525
user-master-h2020-msca-rise-project
user-eu
award_title=Multiple ASpects TrajEctoRy management and analysis; award_number=777695; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/777695; funder_id=00k4n6c32; funder_name=European Commission;
1668090387.969559
1349921
md5:c6ee308df2d7e804f6b4afbb75bafb02
https://zenodo.org/records/7310526/files/MDM2022_demo_paper__Tarlis (3).pdf
public