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), software-based and human-based services.  In this course, we will introduce concepts of IoT and cloud platforms for IoT, data-as-a-service, data concerns, data market places and techniques for developing data intensive services by utlizing data services 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.

The course will provide hand-on experiences via real-world exercises and mini programming projects. The course will  provide a great interaction between students and the instructor.

1. Course Overview
   • Goal: Introduce the course and grading system.
   • Lecture time: 1 hour
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 and Cloud Platforms
   • Goal: discuss Internet of Things (IoT) and cloud platforms for IoT
   • Lecture time: 2 hours
   • Student self-study time: 4 hours
   • Assignment: 4 hours
4. 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
5. Data marketplace models and concepts
   
   • Goal: overview data marketplaces and their service ecosystems
   • Lecture time: 2 hours
   • Student self-study: 3 hours
   • Assignment: 4 hours
6. Discussion of assigments and mini project proposals
   
   • Goal: Reporting, questions and answers for 4 assignments
   • Lecture time: 3 hours
7. Advanced service-based data analytics: concepts and designs
   
   • Goal: present and discuss concepts and designs of advanced service-based data analytics
   • Lecture time: 2 hours
   • Student self-study: 3 hours
   • Mini projects: 4 hours
8. Quality of data -aware data analytics workflows
   
   • Goal: discuss quality of data aware analytics workflows
   • Lecture time: 2 hours
   • Student self-study: 3 hours
   • Mini projects: 4 hours
9. Engineering human-based services in data analytics
   
   • Goal: discuss human-based services and how to utilize them in data analytics
   • Lecture time: 2 hours
   • Student self-study: 3 hours
   • Mini projects: 3 hours
10. Mini projects reporting
    
    • Lecture time: 2 hours
    • Student prepration: 2 hours
11. 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.