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Semantic Web Analysis Graphs: Guided Multidimensional Analysis of Linked Open Data

Autoren
M. Hilal, C. Schütz
Paper
Hila21a (2021)
Zitat
Proceedings of the 27th Americas Conference on Information Systems (AMCIS 2021), August 9-13, 2021, Montreal, Canada, AIS Publ., 10 pages, 2021.
Ressourcen
Kopie  (Senden Sie ein Email mit  Hila21a  als Betreff an dke.win@jku.at um diese Kopie zu erhalten)

Kurzfassung (Englisch)

An increasingly large number of sets of linked open data (LOD), typically in RDF format, are being pub-lished on the Semantic Web. Those data represent a potentially valuable resource for data analysis, particularly online analytical processing (OLAP), which often employs multidimensional (MD) models for conducting MD data analysis. Conducting MD analysis over LOD, however, is not a straightforward task. Most analysts will lack the technical skills to query LOD sources using an unfamiliar query language over data in a format not traditionally associated with MD data analysis. In this paper, we introduce the concept of the semantic web analysis graph (SWAG), which allows experts familiar with the LOD source to plot interesting courses of analysis for other users. We present a proof-of-concept prototype. The results of a usability study show that SWAGs may serve to build intuitive user interfaces.

Keywords: Business intelligence, business analytics, online analytical processing, guided analytics

Download URL: https://aisel.aisnet.org/amcis2021/data_science_decision_support/data_science_decision_support/9