Digging Deep into the Data Mine with DataMiningGrid

Vlado Stankovski, Jernej Trnkoczy, Martin Swain, Werner Dubitzky, Valentin Kravtsov, Assaf Schuster, Thomas Niessen, Dennis Wegener, Michael May, Matthias Röhm, Jürgen Franke

    Research output: Contribution to journalArticle

    24 Citations (Scopus)

    Abstract

    As modern data mining applications increase in complexity, so too do their demands for resources. Grid computing is one of several emerging networked computing paradigms promising to meet the requirements of heterogeneous, large-scale, and distributed data mining applications. Despite this promise, there are still too many issues to be resolved before grid technology is commonly applied to large-scale data mining tasks. To address some of these issues, the authors developed the DataMiningGrid system. It integrates a diverse set of programs and application scenarios within a single framework, and features scalability, flexible extensibility, sophisticated support for relevant standards and different users.
    LanguageEnglish
    Pages69-76
    JournalIEEE INTERNET COMPUTING
    Volume12
    Issue number6
    Publication statusPublished - Nov 2008

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    Data mining
    Grid computing
    Scalability

    Cite this

    Stankovski, V., Trnkoczy, J., Swain, M., Dubitzky, W., Kravtsov, V., Schuster, A., ... Franke, J. (2008). Digging Deep into the Data Mine with DataMiningGrid. 12(6), 69-76.
    Stankovski, Vlado ; Trnkoczy, Jernej ; Swain, Martin ; Dubitzky, Werner ; Kravtsov, Valentin ; Schuster, Assaf ; Niessen, Thomas ; Wegener, Dennis ; May, Michael ; Röhm, Matthias ; Franke, Jürgen. / Digging Deep into the Data Mine with DataMiningGrid. 2008 ; Vol. 12, No. 6. pp. 69-76.
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    Stankovski, V, Trnkoczy, J, Swain, M, Dubitzky, W, Kravtsov, V, Schuster, A, Niessen, T, Wegener, D, May, M, Röhm, M & Franke, J 2008, 'Digging Deep into the Data Mine with DataMiningGrid', vol. 12, no. 6, pp. 69-76.

    Digging Deep into the Data Mine with DataMiningGrid. / Stankovski, Vlado; Trnkoczy, Jernej; Swain, Martin; Dubitzky, Werner; Kravtsov, Valentin; Schuster, Assaf; Niessen, Thomas; Wegener, Dennis; May, Michael; Röhm, Matthias; Franke, Jürgen.

    Vol. 12, No. 6, 11.2008, p. 69-76.

    Research output: Contribution to journalArticle

    TY - JOUR

    T1 - Digging Deep into the Data Mine with DataMiningGrid

    AU - Stankovski, Vlado

    AU - Trnkoczy, Jernej

    AU - Swain, Martin

    AU - Dubitzky, Werner

    AU - Kravtsov, Valentin

    AU - Schuster, Assaf

    AU - Niessen, Thomas

    AU - Wegener, Dennis

    AU - May, Michael

    AU - Röhm, Matthias

    AU - Franke, Jürgen

    PY - 2008/11

    Y1 - 2008/11

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    AB - As modern data mining applications increase in complexity, so too do their demands for resources. Grid computing is one of several emerging networked computing paradigms promising to meet the requirements of heterogeneous, large-scale, and distributed data mining applications. Despite this promise, there are still too many issues to be resolved before grid technology is commonly applied to large-scale data mining tasks. To address some of these issues, the authors developed the DataMiningGrid system. It integrates a diverse set of programs and application scenarios within a single framework, and features scalability, flexible extensibility, sophisticated support for relevant standards and different users.

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    Stankovski V, Trnkoczy J, Swain M, Dubitzky W, Kravtsov V, Schuster A et al. Digging Deep into the Data Mine with DataMiningGrid. 2008 Nov;12(6):69-76.