A Compressed, Inference-enabled Encoding Scheme for RDF Stream Processing
published: July 10, 2017, recorded: May 2017, views: 902
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !