SPARQLytics: Multidimensional Analytics for RDF
| dc.contributor.author | Rudolf, Michael | |
| dc.contributor.author | Voigt, Hannes | |
| dc.contributor.author | Lehner, Wolfgang | |
| dc.contributor.editor | Mitschang, Bernhard | |
| dc.contributor.editor | Nicklas, Daniela | |
| dc.contributor.editor | Leymann, Frank | |
| dc.contributor.editor | Schöning, Harald | |
| dc.contributor.editor | Herschel, Melanie | |
| dc.contributor.editor | Teubner, Jens | |
| dc.contributor.editor | Härder, Theo | |
| dc.contributor.editor | Kopp, Oliver | |
| dc.contributor.editor | Wieland, Matthias | |
| dc.date.accessioned | 2017-06-20T20:24:46Z | |
| dc.date.available | 2017-06-20T20:24:46Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | With the rapid growth of open RDF data in recent years, being able to perform multidimensional analytics with it has become more and more important, in particular for the data analyst performing explorative business intelligence tasks. Existing analytic approaches are often not flexible enough to address the needs of data analysts and enthusiasts with iterative exploratory workflows. In this paper we propose SPARQLytics, a tool that exposes the concepts of multidimensional graph analytics by offering standard OLAP cube operations and generating SPARQL queries. Our evaluation shows that SPARQLytics unburdens data analysts from writing many lines of SPARQL code in iterative data explorations and at the same time it does not impose any overhead to query execution. SPARQLytics fits well with interactive computing tools, such as Jupyter, providing data enthusiasts with a familiar work environment. | en |
| dc.identifier.isbn | 978-3-88579-659-6 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | Datenbanksysteme für Business, Technologie und Web (BTW 2017) | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-265 | |
| dc.title | SPARQLytics: Multidimensional Analytics for RDF | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 60 | |
| gi.citation.startPage | 51 | |
| gi.conference.date | 6.-10. März 2017 | |
| gi.conference.location | Stuttgart | |
| gi.conference.sessiontitle | Query Processing and Languages |
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