loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Noura Azaiez and Jalel Akaichi

Affiliation: ISG-University of Tunis, Tunisia

ISBN: 978-989-758-114-4

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.

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 34.204.0.181

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Azaiez, N. and Akaichi, J. (2015). How Trajectory Data Modeling Improves Decision Making?.In Proceedings of the 10th International Conference on Software Engineering and Applications - Volume 1: ICSOFT-EA, (ICSOFT 2015) ISBN 978-989-758-114-4, pages 87-92. DOI: 10.5220/0005558300870092

@conference{icsoft-ea15,
author={Noura Azaiez. and Jalel Akaichi.},
title={How Trajectory Data Modeling Improves Decision Making?},
booktitle={Proceedings of the 10th International Conference on Software Engineering and Applications - Volume 1: ICSOFT-EA, (ICSOFT 2015)},
year={2015},
pages={87-92},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005558300870092},
isbn={978-989-758-114-4},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Software Engineering and Applications - Volume 1: ICSOFT-EA, (ICSOFT 2015)
TI - How Trajectory Data Modeling Improves Decision Making?
SN - 978-989-758-114-4
AU - Azaiez, N.
AU - Akaichi, J.
PY - 2015
SP - 87
EP - 92
DO - 10.5220/0005558300870092

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.