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Auflistung nach Autor:in "Rudolf, Michael"

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  • Konferenzbeitrag
    The graph story of the SAP HANA database
    (Datenbanksysteme für Business, Technologie und Web (BTW) 2037, 2013) Rudolf, Michael; Paradies, Marcus; Bornhövd, Christof; Lehner, Wolfgang
    Many traditional and new business applications work with inherently graphstructured data and therefore benefit from graph abstractions and operations provided in the data management layer. The property graph data model not only offers schema flexibility but also permits managing and processing data and metadata jointly. By having typical graph operations implemented directly in the database engine and exposing them both in the form of an intuitive programming interface and a declarative language, complex business application logic can be expressed more easily and executed very efficiently. In this paper we describe our ongoing work to extend the SAP HANA database with built-in graph data support. We see this as a next step on the way to provide an efficient and intuitive data management platform for modern business applications with SAP HANA.
  • Konferenzbeitrag
    SPARQLytics: Multidimensional Analytics for RDF
    (Datenbanksysteme für Business, Technologie und Web (BTW 2017), 2017) Rudolf, Michael; Voigt, Hannes; Lehner, Wolfgang
    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.