Moving Beyond Traditional Decision Support Systems: The Power of Trajectory Data Modeling

Moving Beyond Traditional Decision Support Systems: The Power of Trajectory Data Modeling

Noura Azaiez (Department of Computer Science, ISG Institute, Tunis University, Le Bardo, Tunisia), Jalel Akaichi (Department of Computer Science, College of Computer Science, King Khalid University, Abha, Saudi Arabia) and Jeffrey Hsu (Department of Information and Decision Sciences, Silberman College of Business, Fairleigh Dickinson University, Teaneck, NJ, USA)
Copyright: © 2017 |Pages: 14
DOI: 10.4018/IJISSC.2017040104
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Abstract

Integrating the concept of mobility into the professional and organizational realm offers the possibility of reducing geographical disparities related to organization services. The advances made in technology, geographic information systems and pervasive systems equipped with global positioning (GPS) technologies have been able to bring about an evolution from classic data approaches towards the modeling of trajectory data resulting from moving activities of moving objects. As such, trajectory data needs first to be loaded into a Data Warehouse for analysis purposes. However, the traditional approaches used are poorly suited to handle spatio-temporal data features and also the decision making tasks related to mobility issues. Because of this mismatch, the authors propose to move beyond traditional approaches and propose a repository that is able to analyse trajectories of moving objects. Improving decision making and extracting pertinent knowledge with reduced costs and time expended are the main goals of this revised analysis approach. Thus, the authors propose an approach in which they employ the Bottom-up approach to modeling a Decision Support System which is designed to support Trajectory Data. As an example to illustrate this approach, the authors use a creamery and dairy milk mobile cistern application to demonstrate the effectiveness of their approach.
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State Of The Art

Mobile objects are characterized by the different movements and positions which are arranged and fashioned using a time interval. These various positions show the changes and behavior of the mobile object, and result in a model of its trajectory.

In the literature, a great deal of interest has been shown in the management of moving objects. While some are strictly focused on operational IS modeling (Spaccapietra, Parent, Damiani, Macedo, Porto & Vangenot, 2008), others can be more flexible. While database models are not always ideally suited to describing multidimensional aspects, there has been work done on the multidimensional modeling of mobile object behavior (Brakatsoulas, Pfoser & Tryfona, 2004; Wan & Zeitouni, 2005).

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