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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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Dates and versions

hal-01740502 , version 1 (22-03-2018)

Identifiers

  • HAL Id : hal-01740502 , version 1

Cite

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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