SWIM EDX turns all streaming edge data into powerful insights and predictions in real-time without the challenges of big data, using self-learning 'Digital Twins' and an 'edge fabric' of existing devices.
EDX analyzes 'gray' data at the edge, as it's produced, to identify events, issues and relationships, while greatly reducing data volumes and highlight critical information.
EDX discovers edge devices and creates ‘digital twin’ models of real-world devices/systems which self-learn and predict.
SWIM EDX uses existing devices to build a resilient 'edge compute' fabric supporting local data analysis, digital twins, machine learning and edge applications.
SWIM EDX offers a powerful, disruptive approach to edge learning and analytics. Instead of big-data “collect, clean, analyze and learn”, EDX learns on-the-fly as data arrives, at the edge, on existing edge devices.
EDX reduces data volumes by analyzing all streaming edge data, where it's produced. It finds events/correlations/anomalies and predicts critical events in real-time.
EDX analyzes ALL edge data, as fast as it arrives, at the edge, without needing additional expensive big-data, cloud storage or professional services to operate.
EDX creates a ‘digital twin’ model for each real-world data source/object, continuously self-training from real-world data, understanding current behavior and predicting its future.
EDX is self-contained, edge-training and self-managing – no expensive 'big-data' or central storage required.
EDX is secure by design. Information never leaves your environment. EDX protects itself and
EDX supports major industry data protocols and APIs. EDX can run alongside existing applications, data stores, historians, analytics and '
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