Knowledge graph stream processing at the edge

BSTRACTWe present a knowledge graph management system designed to run on Edge computing devices that handles high-frequency data streams. During the design phase, we took into account the inherent limitations of the devices, i.e., limited computing power and storage space, as well as the expectations of applications, e.g., low latency, high throughput, and intelligent data management. This results in a compact, decompression-free, in-memory, streaming-enabled RDF store that supports continuous querying and some forms of reasoning. The system addresses efficient query processing of data continuously arriving at a fast pace and is well-adapted to event-driven applications such as anomaly and risk detection. We empirically emphasize its accuracy, robustness, latency, and throughput properties on a real-world IoT setting originating from the energy management domain.