"Big Data" : Big Gaps of Knowledge in the Field of Internet Science


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SNIJDERS, Chris, Uwe MATZAT, Ulf-Dietrich REIPS, 2012. "Big Data" : Big Gaps of Knowledge in the Field of Internet Science. In: International Journal of Internet Science. 7(1), pp. 1-5. eISSN 1662-5544

@article{Snijders2012Data"-28647, title={"Big Data" : Big Gaps of Knowledge in the Field of Internet Science}, year={2012}, number={1}, volume={7}, journal={International Journal of Internet Science}, pages={1--5}, author={Snijders, Chris and Matzat, Uwe and Reips, Ulf-Dietrich} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/28647"> <dc:language>eng</dc:language> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-08-13T15:01:06Z</dcterms:available> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/28647"/> <dc:rights>deposit-license</dc:rights> <dc:creator>Reips, Ulf-Dietrich</dc:creator> <dc:creator>Matzat, Uwe</dc:creator> <dc:creator>Snijders, Chris</dc:creator> <dcterms:rights rdf:resource="http://nbn-resolving.org/urn:nbn:de:bsz:352-20140905103605204-4002607-1"/> <dc:contributor>Matzat, Uwe</dc:contributor> <dcterms:title>"Big Data" : Big Gaps of Knowledge in the Field of Internet Science</dcterms:title> <dc:contributor>Reips, Ulf-Dietrich</dc:contributor> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-08-13T15:01:06Z</dc:date> <dcterms:bibliographicCitation>International Journal of Internet Science ; 7 (2012), 1. - S. 1-5</dcterms:bibliographicCitation> <dc:contributor>Snijders, Chris</dc:contributor> <dcterms:abstract xml:lang="eng">Research on so-called 'Big Data' has received a considerable momentum and is expected to grow in the future. One very interesting stream of research on Big Data analyzes online networks. Many online networks are known to have some typical macro-characteristics, such as 'small world' properties. Much less is known about underlying micro-processes leading to these properties. The models used by Big Data researchers usually are inspired by mathematical ease of exposition. We propose to follow in addition a different strategy that leads to knowledge about micro-processes that match with actual online behavior. This knowledge can then be used for the selection of mathematically-tractable models of online network formation and evolution. Insight from social and behavioral research is needed for pursuing this strategy of knowledge generation about micro-processes. Accordingly, our proposal points to a unique role that social scientists could play in Big Data research.</dcterms:abstract> <dcterms:issued>2012</dcterms:issued> </rdf:Description> </rdf:RDF>

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