Anomaly Detection

Session 5.6 Data Science

Wednesday, September 13, 2017 - 15:00 to 16:00
Room 6+7


Research & Innovation

Towards a Semantic Outlier Detection Framework in WirelessSensor Networks

Outlier detection in the preprocessing phase of Knowledge Discovery in Databases (KDD) processes has been a widely researched topic for many years. However, identifying the potential outlier cause still remains an unsolved challenge even though it could be very helpful for determining what actions to take after detecting it. Besides, conventional outlier detection methods might still overlook outliers in certain complex contexts. In this article, Semantic Technologies are used to contribute overcoming these problems by proposing the SemOD (Semantic Outlier Detection) Framework. This framework guides the data-scientist towards the detection of certain types of outliers in WSNs (Wireless Sensor Network). Feasibility of the approach has been tested in outdoor temperature sensors and results show that the proposed approach is generic enough to apply it to different sensors, even improving the accuracy of outlier detection as well as spotting their potential cause.


VAT Fraud detection with streaming reasoning

I will be presenting the fraud detection system based on the Cognitum Platform that we have implemented for a state in Brazil. The system is a rule based system that is analyzing a stream of bills in real time.