Visual interactive process monitoring

Abstract

Sensor data has been coined the oil of the 21st century. We present a technique for the visual analysis of multivariate sensor event log data. This technique tackles two challenges: Firstly, in a complex process the relation of causes and effects is often masked by indirections. Secondly, the metrics to measure success might be different from the measures that identify causes. Thus, our approach does not require that all sensor data is equal. Our techniques combines automated and interactive grouping to identify candidate sets sharing properties relevant for cause and effect analysis. Interactive visual probes offer immediate information on the statistical relevance of an identified connection.