In this paper, we present a Big data framework for the prediction of streaming trajectory data by exploiting mined patterns of trajectories, allowing accurate long-term predictions with low latency. In particular, to meet this goal we follow a two-step methodology. First, we efficiently identify the hidden mobility patterns in an offline manner. Subsequently, the trajectory prediction algorithm exploits these patterns in order to prolong the temporal horizon of useful predictions. The experimental study is based on real-world aviation and maritime datasets.