Dr. rer. nat.

Rüping, Stefan

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  • Publication
    A data mining based approach for collaborative analysis of biomedical data
    ( 2014)
    Tsiliki, G.
    ;
    Kossida, S.
    ;
    Friesen, Natalja
    ;
    Rüping, Stefan
    ;
    Tzagarakis, M.
    ;
    Karacapilidis, N.
    Biomedical research becomes increasingly multidisciplinary and collaborative in nature. At the same time, it has recently seen a vast growth in publicly and instantly available information. As the available resources become more specialized, there is a growing need for multidisciplinary collaborations between biomedical researchers to address complex research questions. We present an application of a data mining algorithm to genomic data in a collaborative decision-making support environment, as a typical example of how multidisciplinary researchers can collaborate in analyzing and interpreting biomedical data. Through the proposed approach, researchers can easily decide about which data repositories should be considered, analyze the algorithmic results, discuss the weaknesses of the patterns identified, and set up new iterations of the data mining algorithm by defining other descriptive attributes or integrating other relevant data. Evaluation results show that the proposed approach facilitates users to set their research objectives and better understand the data and methodologies used in their research.
  • Publication
    Mastering data-intensive collaboration through the synergy of human and machine reasoning
    ( 2012)
    Karacapilidis, N.
    ;
    Lau, L.
    ;
    Lee, C.
    ;
    Rüping, Stefan
    Contemporary collaboration settings are often associated with huge, ever-increasing amounts of data, which may vary in terms of relevance, subjectivity and importance. In such settings, collective sense making is crucial for well-informed decision making. This sense making process may both utilize and provide input to intelligent information analysis tools. This workshop aims to bring together researchers and practitioners from different scientific fields and research communities to further explore (i) the synergy between human and machine intelligence, and (ii) larger issues surrounding analytical practices and data sharing practices in the above settings.
  • Publication
    Towards a meaningful analysis of big data enhancing data mining techniques through a collaborative decision making environment
    ( 2012)
    Karacapilidis, N.
    ;
    Rüping, Stefan
    ;
    Tsiliki, G.
    ;
    Tzagarakis, M.
    Arguing that dealing with data-intensive settings is not a technical problem alone, we propose a hybrid approach that builds on the synergy between machine and human intelligence to facilitate the underlying sense-making and decision making processes. The proposed approach, which can be viewed as an innovative workbench incorporating and orchestrating a set of interoperable services, is illustrated through a real case concerning collaborative subgroup discovery in microarray data. Evaluation results, validating the potential of our approach, are also included.
  • Publication
    Data mining based collaborative analysis of microarray data
    ( 2012)
    Tsiliki, G.
    ;
    Kossida, S.
    ;
    Friesen, Natalja
    ;
    Rüping, Stefan
    ;
    Tzagarakis, M.
    ;
    Karacapilidis, N.
    Biomedical research has recently seen a vast growth in publicly and instantly available information, which are often complementary or overlapping. As the available resources become more specialized, there is a growing need for multidisciplinary collaborations between biomedical researchers to address complex research questions. We present an application of a data-mining algorithm to gene-expression data in a collaborative decision-making support environment, as a typical example of how multidisciplinary researchers can collaborate in analyzing and biologically interpreting gene-expression micro array data. Through the proposed approach, researchers can easily decide about which data repositories should be considered, analyze the algorithmic results, discuss the weaknesses of the patterns identified, and set up new iterations of the data mining algorithm by defining other descriptive attributes or integrating other relevant data.
  • Publication
    Building on the synergy of machine and human reasoning to tackle data-intensive collaboration and decision making
    ( 2011)
    Karacapilidis, N.
    ;
    Rüping, Stefan
    ;
    Tzagarakis, M.
    ;
    Poigné, Axel
    ;
    Christodoulou, S.
    This paper reports on a hybrid approach aiming to facilitate and augment collaboration and decision making in data-intensive and cognitively-complex settings. The proposed approach exploits and builds on the most prominent high-performance computing paradigms and large data processing technologies to meaningfully search, analyze and aggregate data existing in diverse, extremely large and rapidly evolving sources. It can be viewed as an innovative workbench incorporating and orchestrating a set of interoperable services that reduce the data-intensiveness and complexity overload at critical decision points to a manageable level, thus permitting stakeholders to be more productive and concentrate on creative activities.