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Saddad, E., A. El-Bastawissy, H. M. O. Mokhtar, and M. Hazman, "Lake Data Warehouse Architecture for Big Data Solutions", International Journal of Advanced Computer Science and Applications, vol. 11, 01, 2020. Abstract


Wageeh, H., H. Mokhtar, and S. Ghoniemy, "Exploring Trusted Relations among Virtual Interactions in Social Networks for Detecting Influence Diffusion", ISPRS Int. J. Geo-Information , vol. 8, issue 9, pp. 415, 2019/09/16. Abstract


Wagih, H. M., H. M. O. Mokhtar, and S. S. Ghoniemy, "Strum: A Semantic Trust-Based Miner Model for Detecting Influential Spreaders in Social Networks", International Conference Big Data Analytics, Data Mining and Computational Intelligence, Porto, Portugal, 16 – 18 July, 2019.
Wagih, H. M., H. M. O. Mokhtar, and S. S. Ghoniemy, "SIMBA: A Semantic-Influence Measurement Based Algorithm for Detecting Influential Diffusion in Social Networks", IEEE 13th International Conference on Semantic Computing (ICSC), Newport Beach, California, Jan. 30 - Feb. 1, 2019.
Wagih, H. M., and H. M. O. Mokhtar, "HBTOnto: An Ontology Model for Analyzing Human Behavior Trajectories", 11th International Conference on Semantics, Knowledge and Grids (SKG 2015), Beijing, August 19-21, 20, 2015.
Wagih, H. M., and H. M. O. Mokhtar, "Ridology: An Ontology Model for Exploring Human Behavior Trajectories in Ridesharing Applications", Recent Advances in Intelligent Systems and Smart Applications, Cham, Springer International Publishing, pp. 553–567, 2020. Abstract

The increasing adoption of social network applications has been an important source of information nowadays. The analysis of human behaviors in social networks has been brought to the forefront of several studies. Location-Based Social Networks (LBSN) are one of the possible means that allow the prediction of human behaviors through the efficient analysis of user's mobility patterns. Despite the remarkable progress in this research direction, however, LBSN is still hindered by the lack of literature defining the semantic aspects of the user's mobility. This research presents a contribution to the latest knowledge representation languages and Semantic Web technologies. We focus on studying human behavior mobility which is the core in location recommendation systems. Bringing to the ridesharing context, an ontology model with its underlying description logics to efficiently annotate human mobility is presented. Finally, experimental results, performed on two location-based social networks, namely, Brightkite (, and BlaBlaCar ( are presented.