Exploring contexts and actions in knowledge processes

This paper presents an approach to automatically discovering the contexts that a knowledge worker is working in and the different types of actions the knowledge worker is executing across contexts. We formulate the two scenarios as two feature selection variations of a clustering problem. The proposed approach is evaluated using realworld data that capture knowledge worker workflow. We evaluate context discovery on gold-standard contexts and action discovery on predictive power of process models, constructed from those actions. The obtained results suggest that we are able to successfully enrich the obtained events log data with additional context and action metadata.