Publication of RDF streams with Ztreamy
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 8798, Page: 286-291
2014
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
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Conference Paper Description
There is currently an interest in the Semantic Web community for the development of tools and techniques to process RDF streams. Implementing an effective RDF stream processing system requires to address several aspects including stream generation, querying, reasoning, etc. In this work we focus on one of them: the distribution of RDF streams through the Web. In order to address this issue, we have developed Ztreamy, a scalable middleware which allows to publish and consume RDF streams through HTTP. The goal of this demo is to show the functionality of Ztreamy in two different scenarios with actual, heterogeneous streaming data.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84908688927&origin=inward; http://dx.doi.org/10.1007/978-3-319-11955-7_36; http://link.springer.com/10.1007/978-3-319-11955-7_36; http://link.springer.com/content/pdf/10.1007/978-3-319-11955-7_36; https://dx.doi.org/10.1007/978-3-319-11955-7_36; https://link.springer.com/chapter/10.1007/978-3-319-11955-7_36
Springer Science and Business Media LLC
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