Basic Informations

C.V

School:  Primary, preparatory and Secondary stages in El- Masra School 
                   Sep, 1992 to Jun, 2004.
                   
College:  - Bachelor Degree of Computer Science, 2008
              Faculty of Computer Science and Information Systems, Cairo University.

Master Degree in Geo-graphical information systems, Cairo University with subject “A Unified Approach for Spatial Data Query”. 2014

Ph.D degree in information systems, Cairo University with subject “Predictive Queries on Moving Object Databases”. 2020   

Master Title

AUnified APPROACH FOR SPATIAL DATA QUERY

Master Abstract

With the rapid development in Geographic Information Systems (GISs) and their applications, more and more geo-graphical databases have been developed by different vendors. However, data integration and accessing is still a big problem for the development of GIS applications as no interoperability exists among different spatial databases. In this paper we propose a unified approach for spatial data query. The paper describes a framework for integrating information from repositories containing different vector data sets formats and repositories containing raster datasets. The presented approach converts different vector data formats into a single unified format (File Geo-Database “GDB”). In addition, we employ “metadata” to support a wide range of users’ queries to retrieve relevant geographic information from heterogeneous and distributed repositories. Such employment enhances both query processing and performance.

PHD Title

Predictive Queries on Moving Objects Databases

PHD Abstract

Future trajectory prediction for moving objects, e.g., vehicles, has a significant impact on many location-based services such as location-aware search, traffic management, mobile advertising, and travel guidance. The existing techniques which predict the future path(s) of moving objects depend mainly on their motions history to perform the prediction process. As a result, these techniques fail when moving objects’ history is unavailable. This thesis aims to present efficient solutions for predicting the trajectories of moving objects without relying on their past trajectories. The proposed solutions include SimilarMove: a similarity-based prediction system for moving object future path, (2) DeepMotions: a deep learning system for moving object future path prediction, and (3) SAM: a spatial attention model for future trajectory prediction. Finally, we conduct extensive experiments on real-life datasets. Experimental results confirm that the proposed systems outperform with respect to performance and accuracy.