Authors:
Noura Azaiez
and
Jalel Akaichi
Affiliation:
ISG-University of Tunis, Tunisia
Keyword(s):
Trajectory Data, Trajectory Data Source, Modeling, Trajectory Data Mart, Bottom-up Approach.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Distributed and Mobile Software Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Knowledge-Based Systems
;
Mobile Technologies
;
Mobile Technologies for Healthcare Applications
;
Neural Rehabilitation
;
Neurotechnology, Electronics and Informatics
;
Software Engineering
;
Symbolic Systems
Abstract:
The incredible progress witnessed in geographic information and pervasive systems equipped with positioning
technologies have motivated the evolving of classic data towards mobility or trajectory data resulting
from moving objects’ displacements and activities. Provided trajectory data have to be extracted, transformed
and loaded into a data warehouse for analysis and/or mining purposes; however, this later, qualified
as traditional, is poorly suited to handle spatio-temporal data features and to exploit them, efficiently, for
decision making tasks related to mobility issues. Because of this mismatch, we propose a bottom-up approach
which offers the possibility to model and analyse the trajectories of moving object activities in order
to improve decision making tasks by extracting pertinent knowledge and guaranteeing the coherence of provided
analysis results at the lowest cost and time consuming. We illustrate our approach through a creamery
trajectory decision support system.