In this work, we integrate the contextual information provided from sensor data and the social relationships collected from online social networks to construct a system, termed BeTracker. We aim to find and track the frequent and representative behaviors for any user-input individual or social structural information. We claim combining physical contacts from sensor data and virtual online interactions can reveal real-life human behaviors. In our BeTracker, we mine the temporal subgraph patterns as the discovered behaviors from sensor-social data transactions. The user-given information, which is the target to observe, can be (a) an individual (to find her daily behaviors), (b) a relational structure (e.g. linear, triangle, or star structure) (to find the frequent and contextual interactions between them), and (c) a relational structure with partially assigned individuals and sequential time periods (to observe their interactions that follow certain temporal order). In the experimental part, we demonstrate promising results of different queries and present the system efficiency of the proposed behavioural pattern mining.