Scientometrics: How to perform a big data trend analysis with ScienceMiner
Autor(en): | Frehe, V. Rugaitis, V. Teuteberg, F. |
Herausgeber: | Plodereder, E. Grunske, L. Ull, D. Schneider, E. |
Stichwörter: | Data acquisition; Data mining; Data visualization; Filtration; Knowledge based systems; Visualization, Control elements; Data mining methods; Design science; Knowledge base; Scientometrics; Status quo; Web-based applications, Big data | Erscheinungsdatum: | 2014 | Herausgeber: | Gesellschaft fur Informatik (GI) | Journal: | Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI) | Volumen: | P-232 | Startseite: | 1699 | Seitenende: | 1710 | Zusammenfassung: | This paper describes the results of the implementation of an application that was designed under the design science principles. The purpose of this application is to identify trends in science. First, the status quo of similar applications as well as the knowledge base about data mining in the field of scientometrics is analyzed. Afterwards, the implementation as well as the evaluation of our application is described. Our web-based application allows to search for contributions (literature and internet, e.g., twitter, news), executes several data mining methods and visualizes the results in seven different ways. Each visualization has some filters and further control elements. It is the first application to provide the complete process from data acquisition to data visualization in an automated way. |
Beschreibung: | Conference of 44. Jahrestagung der Gesellschaft fur Informatik INFORMATIK 2014 - Big Data - Komplexitat meistern - Big Data - Mastering Complexity: 44th Annual Meeting of the Society for Computer Science, INFORMATICS 2014 ; Conference Date: 22 September 2014 Through 26 September 2014; Conference Code:110425 |
ISBN: | 9783885796268 | ISSN: | 16175468 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922561860&partnerID=40&md5=9755d25f768532d3c79781bab7bcfb45 |
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