The emergence of dynamic information sources - including sensor networks - has led to large streams of real-time data on the Web. Research studies suggest, these dynamic networks have created more data in the last three years than in the entire history of civilization, and this trend will only increase in the coming years. With this coming data explosion, real-time analytics software must either adapt or die. This paper focuses on the task of integrating and analyzing multiple heterogeneous streams of sensor data with the goal of creating meaningful abstractions, or features. These features are then temporally aggregated into feature streams. We will demonstrate an implemented framework, based on Semantic Web technologies, that creates feature streams from sensor streams in real-time, and publishes these streams as Linked Data. The generation of feature streams can be accomplished in reasonable time and results in massive data reduction.