This HTML5 document contains 26 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
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n2https://linkeddata.uriburner.com/proxy-iri/
n5https://schema.org/
pwdrhttp://www.w3.org/2007/05/powder-s#
schemahttp://schema.org/
rdfshttp://www.w3.org/2000/01/rdf-schema#
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n4https://linkeddata.uriburner.com/about/id/https/janzz.technology/glossary-en/
xsdhhttp://www.w3.org/2001/XMLSchema#

Statements

Subject Item
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rdf:type
n5:CreativeWork
pwdr:describedby
n4: n6:janzz_glossary_search
schema:headline
Big Data
schema:text
Big data is a blanket term for any collection of data sets so large and complex that it becomes difficult to process using on-hand data management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to e.g. spot trends in the labour market. [Wikipedia]
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rdfs:label
Embedded HTML5 Microdata Statement 55
pwdr:describedby
n4: n6:janzz_glossary_search
rdf:object
Big Data
rdf:predicate
schema:headline
rdf:subject
n2:184a7dbb2771410b87e967b97a43232794d4a045
Subject Item
n2:9790770783a2121f88ba16ea3927664c24dc178b
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rdfs:label
Embedded HTML5 Microdata Statement 57
pwdr:describedby
n4: n6:janzz_glossary_search
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n5:CreativeWork
rdf:predicate
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n2:184a7dbb2771410b87e967b97a43232794d4a045
Subject Item
n2:da6fc932548026fc8cdf27c06df8e187861108d0
rdf:type
rdf:Statement
rdfs:label
Embedded HTML5 Microdata Statement 56
pwdr:describedby
n4: n6:janzz_glossary_search
rdf:object
Big data is a blanket term for any collection of data sets so large and complex that it becomes difficult to process using on-hand data management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to e.g. spot trends in the labour market. [Wikipedia]
rdf:predicate
schema:text
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n2:184a7dbb2771410b87e967b97a43232794d4a045