Strider: An Adaptive, Inference-enabled Distributed RDF Stream Processing Engine

Xiangnan Ren 1, 2 Olivier Curé 1 Li Ke 2 Jérémy Lhez 1 Badre Belabbess 1, 2 Tendry Randriamalala 2 Yufan Zheng 2 Gabriel Képéklian 2
1 LIGM - Laboratoire d'Informatique Gaspard-Monge
2 Atos [Bezons]
Abstract : Real-time processing of data streams emanating from sensors is becoming a common task in industrial scenarios. An increasing number of processing jobs executed over such platforms are requiring reasoning mechanisms. The key implementation goal is thus to efficiently handle massive incoming data streams and support reasoning, data analytic services. Moreover, in an ongoing industrial project on anomaly detection in large potable water networks, we are facing the effect of dynamically changing data and work characteristics in stream processing. The Strider system addresses these research and implementation challenges by considering scalability, fault-tolerance, high throughput and acceptable latency properties. We will demonstrate the benefits of Strider on an Internet of Things-based real world and industrial setting.
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Xiangnan Ren, Olivier Curé, Li Ke, Jérémy Lhez, Badre Belabbess, et al.. Strider: An Adaptive, Inference-enabled Distributed RDF Stream Processing Engine. Proceedings of the VLDB Endowment (PVLDB), VLDB Endowment, 2017, 10 (12), pp.1905 - 1908. ⟨10.14778/3137765.3137805⟩. ⟨hal-01736988⟩

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