Big Data for Context-Aware Computing
Advances in Intelligent Systems and Computing, ISSN: 2194-5365, Vol: 1105 AISC, Page: 168-175
2020
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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.
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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.
Conference Paper Description
In the last few years, we have witnessed a big explosion of the data volume available on the web. Of particular interest in this work is how context-aware computing systems which derive context from data and act accordingly, deal with such huge amounts of data. In this paper we propose a distributed storage based on HBase, which is column-oriented database modeled after Google’s Bigtable. Our generic approach is based of classifying RDF instance by class, we create tables to store the instance data of each class in the ontology.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85080899930&origin=inward; http://dx.doi.org/10.1007/978-3-030-36674-2_18; http://link.springer.com/10.1007/978-3-030-36674-2_18; http://link.springer.com/content/pdf/10.1007/978-3-030-36674-2_18; https://dx.doi.org/10.1007/978-3-030-36674-2_18; https://link.springer.com/chapter/10.1007/978-3-030-36674-2_18
Springer Science and Business Media LLC
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