Mining for Social Processes in Intelligence Data Streams

This work introduces a robust method for identifying and tracking clandestinely operating sub-nets in an active social network. The methodology is based on the Process Query System (PQS) previously applied to process mining in various physical contexts. Given a collection of process descriptions encoding personal and/or coordinated behavior of social entities, we parse a network’s transactional stream for instances of active processes and assign process states to events and functional entities based on a projection of the evidence onto the process models. Our goal is not only to define the social network, but also to identify and track the dynamic states of functionally coherent sub-networks. We apply our methodology to a real world security task— mining a collection of simulated HUMINT and SIGINT intelligence data (the Ali Baba simulated intelligence data set)— and demonstrate superior results both in partitioning and contextualizing the social network.