Generic Semantic Trajectory Data Modelling Approach based on Ontologies

Wided Oueslati, Oumaima Sami, Afef Bahri and Jalel Akaichi
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Wided Oueslati: Ecole Supérieure de Commerce de Tunis, University of Manouba, Manouba, Tunisia†BESTMOD Laboratory, University of Tunis, Tunis, Tunisia
Oumaima Sami: Ecole Supérieure de Commerce de Tunis, University of Manouba, Manouba, Tunisia‡Smart Laboratory, Tunis, Tunisia
Afef Bahri: Ecole Supérieure de Commerce de Tunis, University of Manouba, Manouba, Tunisia‡Smart Laboratory, Tunis, Tunisia
Jalel Akaichi: �Bisha University, Bisha, Saudi Arabia†BESTMOD Laboratory, University of Tunis, Tunis, Tunisia

Journal of Information & Knowledge Management (JIKM), 2024, vol. 23, issue 06, 1-33

Abstract: Advancements in tracking technologies like GPS, RFID and mobile devices have made trajectory data collection widespread. This surge in tracking device usage and location-based services popularity has greatly increased moving object trajectory data availability. The ontological modelling of this kind of data is of paramount importance in understanding and utilising such data effectively. By incorporating maximum semantic data into this model, a variety of essential elements related to mobile object trajectories can be captured. An ontology model rich in semantics not only accurately represents trajectory characteristics but also links them to other relevant elements such as spatial and temporal contexts, movement types and mobile object behaviours. This semantic richness grants the model great adaptability, allowing it to be reused in various contexts related to object mobility and making it generic. Moreover, by integrating this semantic data, the process of analysis and decision-making experiences significant improvement, as it relies on more comprehensive and well-structured information, thereby facilitating informed conclusions and effective strategy implementation. Our objective is to propose a generic ontological model for trajectory data that is rich in semantics and considers the various aspects of moving objects, their movements, their trajectories and their interactions with their environment, aiming to fill the gap identified in other models proposed in the literature.

Keywords: Moving object; trajectory data modelling; trajectory data warehouse; ontology modelling; semantic trajectory data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0219649224500837

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