| dc.contributor.author | Lausen, Georg | |
| dc.contributor.editor | Mayr, Heinrich C. | |
| dc.contributor.editor | Rinderle-Ma, Stefanie | |
| dc.contributor.editor | Strecker, Stefan | |
| dc.date.accessioned | 2020-05-14T07:16:10Z | |
| dc.date.available | 2020-05-14T07:16:10Z | |
| dc.date.issued | 2020 | |
| dc.identifier.isbn | 978-3-88579-698-5 | |
| dc.identifier.issn | 1617-5468 | |
| dc.identifier.uri | http://dl.gi.de/handle/20.500.12116/33122 | |
| dc.description.abstract | Knowledge graphs based on RDF and SPARQL are gaining popularity for integrated semantic representation of structured and unstructured data. As knowledge graphs in practical applications tend to become huge, distributed processing using Apache Spark SQL and Hadoop on top of a compute cluster is attractive. For the corresponding relational representation of a knowledge graph, a simple relational design using only one single table is proposed. Consequently no time consuming relational design considerations are required and newly discovered RDF data can be integrated with nearly no extra additional relational design effort. | en |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | 40 Years EMISA 2019 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-304 | |
| dc.subject | Knowledge Graph | |
| dc.subject | RDF | |
| dc.subject | SPARQL | |
| dc.subject | Apache Spark SQL | |
| dc.subject | Parquet | |
| dc.subject | Hadoop | |
| dc.title | Knowledge Graph Processing Made (more) Simple | en |
| dc.type | Text/Conference Paper | |
| dc.pubPlace | Bonn | |
| mci.reference.pages | 135-138 | |
| mci.conference.sessiontitle | Invited Talk | |
| mci.conference.location | Tutzing, Germany | |
| mci.conference.date | 15.-17. May, 2019 | |
