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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Stream Processing: The Matrix Revolutions
Organization Unit
  • Romana Pernisch
  • Florian Ruosch
  • Daniele Dell'Aglio
  • Abraham Bernstein
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Page Range 15 - 27
Event Title SSWS 2018 Scalable Semantic Web Knowledge Base Systems
Event Type workshop
Event Location Monterey, California, USA
Event Start Date October 9 - 2018
Event End Date October 9 - 2018
Number 2179
Place of Publication Aachen
Abstract Text Analyzing data streams is a vital task in data science. Often, data comes in different shapes such as triples, tuples, relations, or matrices. Traditional stream processing systems, however, only process data in one of these formats. To enable the processing of streams combining different shapes of data, we developed a system that parses SPARQL queries using the Apache Jena parser and transforms them to Apache Flink topologies. With a custom data type and tailored functions, we enabled the integration of matrices in Jena and therefore allowed to mix graphs, relational, and linear algebra in an RDF graph. This provided a proof of concept that queries may be written for static data and – with the usage of the streaming engine Flink – can easily be run on data streams, even if they contain multiple of the aforementioned types.
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