Data fusion is the core of the Nexus fusion engine.  It combines and analyzes the information from multiple data sources, which results in the learning and the real-time recognition of patterns, events and anomalies, while simplifying the task of human operators.  Nexus ensures that no event is missed because of human fatigue or because of data overload, and that only relevant alarms are investigated (low false-positive rate).

In order to detect anomalies, the tide of incoming data must be interpreted. This data is not limited to sensor readings; it can include databases, operator reports, and other sources of information. Nexus fuses these data streams in real-time, issuing alerts when anomalous behaviors are detected. Furthermore, the raw data is mined for patterns that represent information, with the set of patterns representing the knowledge attained by the system. This combination of data mining and fusion forms a decision-support system (DSS) and alleviates the strain on the operator by reducing the influx of information to a manageable level.