A Compressed, Inference-enabled Encoding Scheme for RDF Stream Processing
published: July 10, 2017, recorded: May 2017, views: 910
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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 reformulation. The analysis of results of our prototype emphasize the relevance of our design choices.
Download slides: eswc2017_lhez_RDF_stream_processing_01.pdf (1.4 MB)
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