With 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.

AUTOMATISE: Multiple Aspect Trajectory Data Mining Tool Library

Bernasconi A.;
2022-01-01

Abstract

With 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.
2022
978-1-6654-5176-5
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1161472
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 1
social impact