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

author: Jérémy Lhez, Laboratoire d'Informatique Gaspard-Monge (LIGM)
published: July 10, 2017,   recorded: May 2017,   views: 910


Related Open Educational Resources

Related content

Report a problem or upload files

If 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.
Lecture popularity: You need to login to cast your vote.


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 page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: