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A Compressed, Inference-enabled Encoding Scheme for RDF Stream Processing

Abstract : The number of sensors producing data streams at a high velocity keeps increasing. This paper describes an attempt to design an inference-enabled, distributed, fault-tolerant framework targeting RDF streams in the context of an industrial project. Our solution gives a special attention to the latency issue, an important feature in the context of providing reasoning services. Low latency is attained by compressing the scheme and data of processed streams with a dedicated semantic-aware encoding solution. After providing an overview of our architecture, we detail our encoding approach which supports a trade-off between two common inference methods, i.e., materialization and query reformula-tion. The analysis of results of our prototype emphasize the relevance of our design choices.
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Submitted on : Thursday, March 22, 2018 - 9:26:53 AM
Last modification on : Friday, September 16, 2022 - 1:56:07 PM
Long-term archiving on: : Thursday, September 13, 2018 - 7:12:25 AM


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  • HAL Id : hal-01740502, version 1


Jérémy Lhez, Xiangnan Ren, Badre Belabbess, Olivier Curé. A Compressed, Inference-enabled Encoding Scheme for RDF Stream Processing. ESWC, 2017, Portorož, Slovenia. ⟨hal-01740502⟩



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