Using Criteria Reconstruction for Low-sampling Trajectories as a Tool for Analytics | Zendy

Edison Camilo Ospina | Zendy; F. Javier Moreno | Zendy; Iván Amón Uribe | Zendy

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Using Criteria Reconstruction for Low-sampling Trajectories as a Tool for Analytics

Author(s) -

Edison Camilo Ospina,

F. Javier Moreno,

Iván Amón Uribe

Publication year - 2015

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2015.05.256

Subject(s) - computer science , analytics , trajectory , sampling (signal processing) , data warehouse , dimension (graph theory) , data mining , data science , visual analytics , order (exchange) , data analysis , visualization , computer vision , physics , mathematics , filter (signal processing) , finance , astronomy , pure mathematics , economics

Mobile applications equipped with Global Positioning Systems have generated a huge quantity of location data with sampling uncertainty that needs to be handled and analyzed. Those location data can be ordered in time to represent trajectories of moving objects. The data warehouse approach based on spatio-temporal data can help on the analysis. For this reason, we consider the problem of personalized reconstruction of low-sampling trajectories and include the criteria of movement as a dimension of analysis in a trajectory data warehouse. We enhance the analytics using dimensional modelling and graphical analysis in order to provide mechanisms to help decision makers. For example, analysts may formulate queries such as What are the top 5 most traversed streets between 07:00:00 am and 09:00:00 pm on August 9, 2014 (Saturday) if the trajectories are reconstructed using the touristic criterion? The answer to this query may help users to identify, e.g., city bottlenecks

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