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

Noura Azaiez, Jalel Akaichi and Jeffrey Hsu
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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
Jeffrey Hsu: Department of Information and Decision Sciences, Silberman College of Business, Fairleigh Dickinson University, Teaneck, NJ, USA

International Journal of Information Systems and Social Change (IJISSC), 2017, vol. 8, issue 2, 74-87

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jissc0:v:8:y:2017:i:2:p:74-87

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