Ontology-based Trajectory Data Warehouse Conceptual Model Marwa Manaa 1 and Jalel Akaichi 2 1 Université de Tunis, ISG, BESTMOD, 2000, Le Bardo - TUNISIA 2 College of Computer Science, King Khaled University, Abha - Saudi Arabia {manaamarwa,j.akaichi}@gmail.com Abstract. The enormous evolution of positioning technologies and re- mote sensors is leading to big amounts of disparate mobility data. Col- lected mobility data generates the need of modelling of such behaviour and the understanding of them which gave the rise of different models achieved either by classical conceptual modelling or by those based on ontology. Modelling and analysing trajectory data are still challenging because of the heterogeneity of trajectory data models and the complex- ity of establishing choices about domain’s consensual knowledge. To fulfil this objective, we propose a generic ontology that explains the seman- tics of these data and we define a trajectory data warehouse conceptual model based on the shared ontology in order to analyse trajectory data going from users’ short transactions to complex queries involving decision makers. The shared ontology that we propose is an OWL-DL formalism that covers common structures encountered in trajectories.We illustrate our work with a real case study. Keywords: Data warehouse, ontology, OWL-DL formalism, semantic modelling, trajectory data. 1 Introduction Data driven scientific discovery approach has been an important paradigm for computing in many central areas including Internet of Things, social networks, remote sensors, etc. Under this paradigm, mobility data commonly named tra- jectory data is the core that reveals the details of instantaneous behaviours conducted by mobile entities. Basically, trajectory data is a record of the evolu- tion of the position (perceived as a point) of an object that is moving in space during a given time interval in order to achieve a given goal [9]. Actually, work- ing on this field is a fresh but active matter which is essentially due to the rise of applications, pervasive devices and positioning technologies offering mobility data. The management of collected mobility data is expected to extract useful knowledge about moving object and facilitates, then, the understanding of their behaviour from analytic and cognitive perspectives. Therefore, collected mobility data gave rise to different trajectory data models achieved either by enhancing classical "conceptual models" used for designing database schema or by propos- ing new ones such as "ontology-based representations". Yet, disparate trajectory