Overview in sensor networks and Basics in the field of data quality and quality management. The students are able to deal with the special requirements of sensor networks and can make assessments regarding data quality. The students know possibilities for the qualitative improvement of acquired data.

Data management, database systems

The following topics will be covered in the course:

• Special requirements for sensor networks (energy, CPU, sizes, costs, architectures, synchronization, ...)
• Data quality (complete, reliable, consistent, accurate, ...)
• Application examples: Environmental sensing, fire alarm systems, vital function monitoring, climate control, earthquake prediction, transportation monitoring, ...).
• Methods for improving data quality

The following basic literature will be used in the course:

• Hsu H-H.: "Big Data Analyticals for Sensor-Network Collected Intelligence (Intelligent Data-Centric Systems: Sensor Collected Intelligence)", Morgan Kaufmann, 1st edition, 2017;
• Sadiq S.: "Handbook of Data Quality", Springer, 1st edition, 2015;
• Sebastian-Coleman L: "Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework," Morgan Kaufman, 1st edition, 2013;
• Loshin D.: "The Practitioner's Guide to Data Quality Improvement", Morgan Kaufmann, 1st edition, 2010;

Lecture and moderated exercises

Final grade comprised of

• Class participation
• Final exam