Framework for process discovery from sensor data

Process mining can give valuable insights into how real-life activities are performed when extracting meaningful activities instances from raw sensor events. However, in many cases, the event data generated during the execution of a process is at a much lower level of abstraction, and, in some cases, even continuous, e.g., sensor data. This paper presents a framework to discover activities and process models from event location sensor data. The framework is flexible enough to be applied to any data set from raw sensor data.