What is the Impact of Mobility Data Integration on Decision Support Systems' Modelling and Evolution?

Abstract

Classic data seems unable to keep pace with the technology era. In fact, the incredible progress related to geographic information systems, pervasive computing, and positioning technologies have motivated classic data to evolve towards new data kind called mobility data. For decisional purposes, these later have to be analysed; therefore, their integration into a decision support system becomes a must. However, the data warehouse used to store classical data seems to be inadequate for mobility data storage and analysis. This gave the birth of a new central repository type called trajectory data warehouse which is able to support mobility data extraction, transformation, loading, and analysis and/or mining. As classic data warehouses, the trajectory one often changes its content as well as its structure for various reasons such as the organizational business processes progressing over time, the evolving needs of decision makers that lead to DW structure enrichment with additional analyses axes, or even the incompleteness of needs initially captured during the design phase of the DW . This work proposes a survey that gathers the research works that deal with the issue of trajectory data warehouse modelling and evolution; then the authors present comparative study of the proposed solutions.

Moving Beyond Traditional Decision Support Systems

“…As such, the quality of decision making is strongly dependent on the Decision Support System (DSS) architecture being used (Nfaoui, El Beqqali, Ouzrout & Bouras, 2009;Wang and Radosevich, 2010;Tynjala, 2012;De Felice and Petrillo, 2013;Kim and Eom, 2016). Hence, the creation of a Trajectory Decision Support System (TDSS) that is able to perform analysis features becomes a must (Azaiez and Akaichi, 2016). All the Trajectory Data (TD) collected from the mobile object activities are stored in a data model that can be termed a Trajectory Data Source (TDSrc) model.…”