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 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 marketplaces 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. 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.

1. Course Overview
   • Goal: Introduce the course and grading system.
   • Lecture time: 1 hour (10.3.2017, Library, Inst. f. Informationssysteme, Argentinierstr. 8, 3. floor)
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, datalake, data governance
   
   • 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. Big data 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
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.