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Auflistung nach Autor:in "Meyerhenke, Henning"

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  • Zeitschriftenartikel
    Scaling up network centrality computations – A brief overview
    (it - Information Technology: Vol. 62, No. 3-4, 2020) Grinten, Alexander van der; Angriman, Eugenio; Meyerhenke, Henning
    Network science methodology is increasingly applied to a large variety of real-world phenomena, often leading to big network data sets. Thus, networks (or graphs) with millions or billions of edges are more and more common. To process and analyze these data, we need appropriate graph processing systems and fast algorithms. Yet, many analysis algorithms were pioneered on small networks when speed was not the highest concern. Developing an analysis toolkit for large-scale networks thus often requires faster variants, both from an algorithmic and an implementation perspective. In this paper we focus on computational aspects of vertex centrality measures. Such measures indicate the (relative) importance of a vertex based on the position of the vertex in the network. We describe several common (and some recent and thus less established) measures, optimization problems in their context as well as algorithms for an efficient solution of the raised problems. Our focus is on (not necessarily exact) performance-oriented algorithmic techniques that enable significantly faster processing than the previous state of the art – often allowing to process massive data sets quickly and without resorting to distributed graph processing systems.
  • Zeitschriftenartikel
    The Collaborative Research Center FONDA
    (Datenbank-Spektrum: Vol. 21, No. 3, 2021) Leser, Ulf; Hilbrich, Marcus; Draxl, Claudia; Eisert, Peter; Grunske, Lars; Hostert, Patrick; Kainmüller, Dagmar; Kao, Odej; Kehr, Birte; Kehrer, Timo; Koch, Christoph; Markl, Volker; Meyerhenke, Henning; Rabl, Tilmann; Reinefeld, Alexander; Reinert, Knut; Ritter, Kerstin; Scheuermann, Björn; Schintke, Florian; Schweikardt, Nicole; Weidlich, Matthias
    Today’s scientific data analysis very often requires complex Data Analysis Workflows (DAWs) executed over distributed computational infrastructures, e.g., clusters. Much research effort is devoted to the tuning and performance optimization of specific workflows for specific clusters. However, an arguably even more important problem for accelerating research is the reduction of development, adaptation, and maintenance times of DAWs. We describe the design and setup of the Collaborative Research Center (CRC) 1404 “FONDA -– Foundations of Workflows for Large-Scale Scientific Data Analysis”, in which roughly 50 researchers jointly investigate new technologies, algorithms, and models to increase the portability, adaptability, and dependability of DAWs executed over distributed infrastructures. We describe the motivation behind our project, explain its underlying core concepts, introduce FONDA’s internal structure, and sketch our vision for the future of workflow-based scientific data analysis. We also describe some lessons learned during the “making of” a CRC in Computer Science with strong interdisciplinary components, with the aim to foster similar endeavors.