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H. Truong:
"Quantifying and Detecting Incidents in IoT Big Data Analytics";
Talk: Dagstuhl Seminar 17441: Big Stream Processing Systems, Schloss Dagstuhl, Wadern, Germany (invited); 2017-10-29 - 2017-11-03.

Systems for IoT Big data analytics are extremely complex. Different software components at different software stacks from different infrastructures and providers are involved in handling different types of data. Various types of incidents may occur during execution of such a big data analytics due to problems occurring in software stacks, the data itself, and processing algorithms. Here incidents reflect unexpected situations that might happen within data themselves, machine learning algorithms, data pipelines, and underlying big data services and computing platforms. It is important to address any incident that prevents the pipeline running correctly or producing the expected quality of analytics. In this presentation, we show the motivation for quantifying, monitoring and analytics of incidents in IoT big data analytics systems and discuss our plan to tackle this important research.