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• W2893721107 abstract "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." @default.
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• W2893721107 date "2018-10-09" @default.
• W2893721107 modified "2023-09-23" @default.
• W2893721107 title "Stream Processing: The Matrix Revolutions." @default.
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• W2893721107 doi "https://doi.org/10.5167/uzh-153459" @default.
• W2893721107 hasPublicationYear "2018" @default.
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