Viola: Detecting Violations of Behaviors from Streams of Sensor Data

Sensor networks and the Internet of Things enable the easy collection of environmental data. With this data it is possible to perceive the activities carried out in an environment. For example, in healthcare, sensor data could be used to identify and monitor the daily routine of people with dementia. In fact, changes in routines could be a symptom of the worsening of the disease. Streaming conformance checking techniques aim at identifying in real-time, from a stream of events, whether the observed behavior differs from the expected one. However, they require a stream of activities, not sensor data. The artifact-driven process monitoring approach combines the structure of the control-flow with the data in an E-GSM model. This paper presents Viola, the first technique capable of automatically mining an E-GSM model from a labeled sensor data log, which is then suitable for runtime monitoring from an unlabeled sensor stream to accomplish our goal (i.e., streaming conformance checking). This approach is implemented and has been validated with synthetic sensor data and a real-world example.