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Information and computer science school, Nile University, Cairo, Egypt

The Eleventh International Conference on Model & Data Engineering (MEDI) will be held from 21 to 24 November 2022 in Cairo, Egypt. Its main objective is to provide a forum for the dissemination of research accomplishments and to promote the interaction and collaboration between the models and data research communities. MEDI’2022 provides an international platform for the presentation of research on models and data theory, the development of advanced technologies related to models and data, and their advanced applications. This international scientific event, initiated by researchers from Middle eastern countries, aims also at promoting the creation of north-south scientific networks, projects, and faculty/student exchanges.

Email.: w.al-atabany@nu.edu.eg

Modelling and Models Engineering, - Modelling languages and related standards, - Modelling in software and complex system engineering, - Formal methods, analysis, verification & validation, - Model-based testing and performance analyses, - Ontology-based modelling, - Heterogeneous modelling, model integration, interoperability, model transformation, - Models as services and models monitoring, - Modelling for reuse, dependability, and maintainability, - Models for security, trust, risk, - Models for Big Data & NoSQL databases, Data Engineering, - New models and architectures for databases, - Heterogeneous data, data Integration, Interoperability, - Data warehouses, OLAP, data mining, and Analytics, - Extending Data Warehousing Technology, - Database system Internals, performance analysis, self-tuning benchmarking, and testing, - Database security, personalization, recommendation, - Web databases, ontology-based databases, - Service-based data management, - Service-oriented applications, - Urbanization of database applications, - Data models for IA techniques and Machine Learning, Applications and tools, - Industry transfer, experiences, - Applications of modelling for Data Management, - Modelling tools and experimentation, - Lessons learned and reports related to models/data engineering,