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Auflistung nach Autor:in "Rostami, M. Ali"

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  • Textdokument
    Big graph analysis by visually created workflows
    (BTW 2019, 2019) Rostami, M. Ali; Peukert, Eric; Wilke, Moritz; Rahm, Erhard
    The analysis of large graphs has received considerable attention recently but current solutions are typically hard to use. In this demonstration paper, we report on an effort to improve the usability of the open-source system Gradoop for processing and analyzing large graphs. This is achieved by integrating Gradoop into the popular open-source software KNIME to visually create graph analysis workflows, without the need for coding. We outline the integration approach and discuss what will be demonstrated.
  • Journal
    BIGGR: Bringing Gradoop to Applications
    (Datenbank-Spektrum: Vol. 19, No. 1, 2019) Rostami, M. Ali; Kricke, Matthias; Peukert, Eric; Kühne, Stefan; Wilke, Moritz; Dienst, Steffen; Rahm, Erhard
  • Textdokument
    Graph Sampling with Distributed In-Memory Dataflow Systems
    (BTW 2021, 2021) Gomez, Kevin; Täschner, Matthias; Rostami, M. Ali; Rost, Christopher; Rahm, Erhard
    Given a large graph, graph sampling determines a subgraph with similar characteristics for certain metrics of the original graph. The samples are much smaller thereby accelerating and simplifying the analysis and visualization of large graphs. We focus on the implementation of distributed graph sampling for Big Data frameworks and in-memory dataflow systems such as Apache Spark or Apache Flink and evaluate the scalability of the new implementations. The presented methods will be open source and be integrated into Gradoop, a system for distributed graph analytics.