Vehicle trajectory data warehouse

Arfaoui, Nouha; Akaichi, Jalel

doi:10.1145/3018896.3056790

Cited by 3 publications

(6 citation statements)

References 13 publications

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“…The work (Spaccapietra, Parent, Damiani, Macedo, Porto & Vangenot, 2008) is among works which investigated the problem of trajectory data conceptual modeling of trajectories. It presents the basis of the work (Arfaoui & Akaichi, 2011) where authors are concentrated on the modeling of the animals' trajectories. In fact, they proposed two ways of trajectory data models to build a TDW model allowing the storage of trajectory data.…”

Section: Discussionmentioning

confidence: 99%

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

Azaiez

Akaichi

2016

International Journal of Information Systems in the Service Sector

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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.

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Section: Discussionmentioning

confidence: 99%

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

Azaiez

Akaichi

2016

International Journal of Information Systems in the Service Sector

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“…of Stops by trajectory D Car [30] NbPath: No. of paths constituting T D Herd [30] MaxSpeed: find maximum speed of the herd D Herd [30] MinSpeed: it corresponds to minimum speed of the herd D Herd [30] CoveredDistance: corresponds to the covered distance traveled by the animals during their displacement D Herd [30] During: duration of the trajectory D Herd [31] Frequent pattern of trajectories: to discover different pattern of MO N/A Car [21] Hot Zone: return the location that visitors spend more than 3 min at specific location N/A People The table describes the different measures stored in a fact table and can be answered from the TDW according to the applications type T: refers to Trajectory; No, refers to number; D, Distributive; H, Holistic; A, Algebraic granularity increased. The study in [3] follows the same approach to solve the double count problem for measures such as count users.…”

Section: Transformation Processmentioning

confidence: 99%

“…The studies in the [18,24,25] model the TDW using the star schema model, which consists of spatial, temporal dimensions with different level of hierarchies, and one fact table. Furthermore, the studies in [29,30] use the same model with additional non-spatial dimensions to provide more analysis details. The study in [9] models a semantic TDW and aims to provide more knowledge of the moving object's behaviors.…”

Section: Trajectory Data Warehouse Modelsmentioning

confidence: 99%

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A Survey on Trajectory Data Warehouse

2019

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Advanced technologies in location acquisition allow us to track the movement of moving objects (people, planes, vehicles, animals, ships, ...) in geographical space. These technologies generate a vast amount of trajectory data (TD). Several applications in different fields can utilize such TD, for example, traffic management control, social behavior analysis, wildlife migrations and movements, ship trajectories, shoppers behavior in a mall, facial nerve trajectory, location-based services and many others. Trajectory data can be mainly handled either with Moving Object Databases (MOD) or Trajectory Data Warehouse (TDW). In this paper, we aim to review existing studies on storing, managing, and analyzing TD using data warehouse technologies. We propose a framework that aims to provide the requirements for building the TDW. Furthermore, we discuss different applications using the TDW and how these applications utilize the TDW. We address some issues with existing TDWs and discuss future work in this field.

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“…For these reasons, the authors implement the proposed TDW while using operations associated with traditional DWs: namely, a pre-processing phase to transform data to be loaded, and a phase employing aggregation functions, necessary to support OLAP operations. Arfaoui and Akaichi (2011) presented a trajectory model related to the displacement of a herd, which allows for the building of a DW containing trajectory data. The model is generated by following and monitoring herd movements.…”

Section: State Of the Artmentioning

confidence: 99%

Moving Beyond Traditional Decision Support Systems

Azaiez

Akaichi

Hsu

2017

International Journal of Information Systems and Social Change

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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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Vehicle trajectory data warehouse

Cited by 3 publications

References 13 publications

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

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

A Survey on Trajectory Data Warehouse

Moving Beyond Traditional Decision Support Systems

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