The objective of this course is to introduce new concepts and techniques for developing and engineering advanced services in emerging distributed computing systems including IoT (Internet of Things), network functions, cloud services, blockchain, machine learning and  human-based services.  In this course, we will examine the roles of IoT, cloud services, blockchain,   data-as-a-service, data concerns, data marketplaces, machine learning for advanced elastic services. We study and implement techniques for developing such services by utlizing IoT data and other types of data  with compute services in cloud environments.  Furthermore, we will investigate human-based services in engineering advanced data analytics and how to combine them with data and compute services. All of them create emerging hybrid computing systems for various important domains, such as smart cities, predictive maintenance, etc.

The course will provide hand-on experiences via real-world exercises and mini programming projects. This follows project-based course approach. The course will provide a great interaction between students and the instructor. Students are expected to produce realistic applications and services to demonstrate their selected scenarios.

In the course, students can also use Google Cloud for real experiments.

1. Course Overview
   • Goal: Introduce the course and grading system.
   • Lecture time: 1 hours
2. Emerging dynamic distributed systems and challenges for advanced services engineering
   
   • Goal: introducing the context of the computing systems in the course and problems in advanced services engineering
   • Lecture time: 2 hours
   • Student self-study time: 3 hours
3. IoT, Cloud, Blockchain and Machine Learning for advanced services
   
   • Goal: discuss Internet of Things (IoT),  cloud platforms, blockchain, and machine learning  for services
   • Lecture time: 2 hours
   • Student self-study time: 4 hours
   • Assignment: 4 hours
4. Discussion of scenario
   
   • Goal:  Presentation of scenarios
   • Lecture time: 2 hours
5. Data-as-a-Service concepts, models and engineering
• Goal: discuss data-as-a-service concepts, introduce data concerns and their roles in data analytics service engineering
• Lecture time: 2 hours
• Student self-study time: 3 hours
• Assignment: 4 hours
6. Service engineerings in multi clouds
   
   • Goal:  services engineering, complex services models
   • Assignment: 4 hours
7. Discussion of assigments and mini project proposals
   
   • Goal: Reporting, questions and answers for 4 assignments
   • Lecture time: 2 hours
8. Big data: concepts, systems and platforms
   
   • Goal: present and discuss concepts and designs of big data systems for advanced service-based data analytics
   • Lecture time: 2 hours
   • Student self-study: 3 hours
   • Mini projects: 4 hours
9. Data analytics and  Quality of data -aware big data analytics
   
   • Goal: discuss big data, quality of data aware analytics
   • Lecture time: 2 hours
   • Student self-study: 3 hours
   • Mini projects: 4 hours
10. Engineering human-machine interactions in advanced services
    
    • Goal: discuss human-machine interactions in services and how to utilize them analytics
    • Lecture time: 2 hours
    • Student self-study: 3 hours
    • Mini projects: 3 hours
11. Mini projects reporting
    
    • Lecture time: 2 hours
    • Student prepration: 2 hours
12. Final oral examination
    
    • Lecture time: 8 hours

The total marks for this course is 100 points

• Participation and discussion: 10 points
• Course assignments: 40 points
• Programming mini projects: 20 points
• Final oral examination: 30 points

Grading mapping

• 100 -90 points ; 1 (Sehr gut)
• 75-89                :  2 (Gut)
• 56: 74              :   3 (Befriedigend)
• 40-55               :   4  (Genügend )                   
• 0-40 points     :   5  (Nicht Genügend)

It is expected that students have finished courses in distributed systems and advanced internet computing.