Publications

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A
Mohamed, D., A. El-Kilany, and H. M. O. Mokhtar, "Academic Articles Recommendation Using Concept- Based Representation", Intelligent Systems Conference (IntelliSys), 3-4 September, 2020.
B
Ismail, A. S., lHaytham Al-Feel, and H. M. O. Mokhtar, "Bridging the Gap for Retrieving DBpedia Data", 4th International Conference on e-Technologies and Networks for Development, Lodz University of Technology, Poland, September 21-23, 2015.
C
Moniem, D. A. A. E., and H. M. O. Mokhtar, "Check2: A Framework for Fake Check-in Detection", Intelligent Computing Proceedings of the 2019 Computing Conference, Vol. 2, London, United Kingdom, Springer, pp. 1-19, 2019.
Gomaa, I., and H. M. O. Mokhtar, "Continuous Skyline Queries in Distributed Environment", International Journal of Data Science, vol. 4, pp. 45-62, 2019.
D
Abdalla, M., A. Hendawi, H. M. O. Mokhtar, N. ElGamal, J. Krumm, and M. Ali, "DeepMotions: A Deep Learning System For Path Prediction Using Similar Motions.", IEEE Access, vol. 8, pp. 23881 - 23894, 2020/01/15. Abstract

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Mohy, N. N., H. M. O. Mokhtar, and M. E. El-Sharkawi, "Delegation Enabled Provenance-based Access Control Model", Science and Information Conference 2015 {SAI}, London, UK, June 28 - 30, 2014, 2015. Abstract

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El-Dawy, E., H. M. O. Mokhtar, and A. El-Bastawissy, "Directional Skyline Queries", Data and Knowledge Engineering - Third International Conference, {ICDKE} 2012, Wuyishan, Fujian, China, November 21-23, 2012. Proceedings, pp. 15–28, 2012. Abstract

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Ossama, O., H. M. O. Mokhtar, and undefined, "Dynamic k-means: a clustering technique for moving object trajectories", {IJIIDS}, vol. 6, no. 4, pp. 307–327, 2012. Abstract

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E
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

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F
Hasan, M. M., and H. M. O. Mokhtar, "F2F: Friend-to-Friend Semantic path Recommendation", International Journal of Applied Engineering Research, vol. 12, issue 13, pp. 3724-3729, 2017.
Eldawy, E. E. O., A. Hendawi, M. Abdalla, and H. M. O. Mokhtar, "FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection", isprs International Journal of Geo-Information, vol. 10, issue 11, pp. 1-22, 2021.
Eldawy, E. E. O., A. Hendawi, M. Abdalla, and H. M. O. Mokhtar, "FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection", isprs International Journal of Geo-Information, vol. 10, issue 11, pp. 1-22, 2021.
Eldawy, E. E. O., A. Hendawi, M. Abdalla, and H. M. O. Mokhtar, "FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection", isprs International Journal of Geo-Information, vol. 10, issue 11, pp. 1-22, 2021.
Eldawy, E. E. O., A. Hendawi, M. Abdalla, and H. M. O. Mokhtar, "FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection", isprs International Journal of Geo-Information, vol. 10, issue 11, pp. 1-22, 2021.
H
Abdalla, M., H. M. O. Mokhtar, and N. ElGamal, "HarmonyMoves: A Unified Prediction Approach for Moving Object Future Path", (IJACSA) International Journal of Advanced Computer Science and Applications, vol. 11, issue 1, pp. 637-644, 2020.
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.
I
Saadon, A. B. G., and H. M. O. Mokhtar, "iiHadoop: An Asynchronous Distributed Framework for Incremental Iterative Computations", Journal of Big Data, vol. 4, issue 1, pp. 24-54, 2017.
M.Hassan, M., and H. M. O. Mokhtar, "Investigating autism etiology and heterogeneity by decision tree algorithm", Informatics in Medicine Unlocked, vol. 16, 2019.
K
Menshawy, D. E. L., H. M. O. Mokhtar, and O. Hegazy, "A Keystroke Dynamics Based Approach for Continuous Authentication", Beyond Databases, Architectures, and Structures - 10th International Conference, {BDAS} 2014, Ustron, Poland, May 27-30, 2014. Proceedings, pp. 415–424, 2014. Abstract
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L
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

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M
Habib, W. M. A., H. M. O. Mokhtar, and M. E. El-Sharkawi, "MapReduce Algorithms for Processing Universal Quantifier Queries", 2014 {IEEE} 7th International Conference on Cloud Computing, Anchorage, AK, USA, June 27 - July 2, 2014, pp. 578–585, 2014. Abstract

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N
Mokhtar, H. M. O., and N. A. Hussein, "A novel mechanism for enhancing software transactional memory", 18th International Database Engineering {&} Applications Symposium, {IDEAS} 2014, Porto, Portugal, July 7-9, 2014, pp. 278–283, 2014. Abstract
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P
Habib, W. M. A., H. M. O. Mokhtar, and M. E. El-Sharkawi, "Processing universal quantification queries using MapReduce", International Conference on Big Data and Smart Computing, {BIGCOMP} 2014, Bangkok, Thailand, January 15-17, 2014, pp. 149–154, 2014. Abstract

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Q
Ismail, A. S., H. Al-Feel, and H. M. O. Mokhtar, "Querying DBpedia Using HIVE-QL", 14th International Conference on Telecommunications and Informatics (TELE-INFO '15), Sliema,Malta, August 17-19, 20, 2015.
R
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 (https://snap.stanford.edu/data), and BlaBlaCar (https://www.blablacar.co.uk/) are presented.