The main character of the course is necessary for students who use the techniques, and platforms for informing intelligent and interoperable applications that request messages with the help of Artificial Intelligence and Engineering approaches Learn. Emphasis will be placed on issues related to the scalability of information systems, and their management including mechanisms for monitoring, self-management and fault tolerance in the full life cycle of information systems services. In addition, issues related to the architectures of interconnected information systems services as well as the techniques of implementation and use of the aforementioned services will be analyzed. Through this course, students are expected to acquire important technical skills related to the modeling of intelligent information systems, and will learn to design and implement large-scale information systems consisting of complex services.
Lambda architectures for the interconnection of information systems services
Approaches to storing, using and analyzing data through service streams. Batch layer for storing data on a medium, Serving layer for indexing and Real-time processing layer.
Information system approaches as a service
Lists of services and mechanisms for finding, selecting, executing, supervising, evaluating and costing. Methodology for modeling and development of information systems as a service.
Platform as a service (Laboratory)
Platform approaches for the implementation of information systems as a service. Serverless computing architectures. A lab that focuses on programming, configuring, and executing applications using the Google AppEngine platform and the Apache OpenWhisk platform.
Self-management information systems
Real-time monitoring and analysis of infrastructure data and information system data. Scaling, flexibility and fault tolerance approaches.
Artificial intelligence and machine learning for information systems management
Services for creating information systems development profiles and changes at runtime using artificial intelligence and machine learning approaches (neural networks, reinforcement learning).
Cloud computing and information systems
Modeling and transition of information systems to cloud and storage infrastructure. Dimensioning of necessary resources and real-time feedback techniques for adapting the infrastructure based on the needs of the information systems.
Introduction to information systems interoperability
Basic principles, definitions and benefits. Main approaches and requirements. International standards and initiatives.
European Interoperability Framework
Objectives, basic principles. Interoperability levels. Conceptual model.
Interoperable public digital services
Interoperable digital services design methodology. Software component maturity assessment models.
Interoperability of information systems
Interoperability of electronic procurement systems. Interoperability of e-health systems.
- Aurelien Geron, Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2019
- Peter Sbarski, Serverless Architectures on AWS, 2017
- Cagatay Gurturk, Building Serverless Architectures, 2017
- John Arundel and Justin Domingu, Cloud Native DevOps with Kubernetes: Building, Deploying, and Scaling Modern Applications in the Cloud, 2019