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Auflistung nach Autor:in "Granitzer, Michael"

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  • Konferenzbeitrag
    Intelligent Maps and Information Landscapes. Two New Approaches to Support Search and Retrieval of Environmental Information Objects
    (Environmental Communication in the Information Society - Proceedings of the 16th Conference, 2002) Tochtermann, Klaus; Sabol, Vedran; Kienreich, Wolfgang; Granitzer, Michael; Becker, Jutta
    In this paper, we present how two different knowledge retrieval techniques can be applied to improve search and retrieval for environmental information objects. Both approaches have in common that they provide users with as much guidance as possible. Our first approach capitalises on geographical metadata and intelligent maps as retrieval tool. The idea is to start a query with an intelligent map and to systematically narrow the scope of interest by choosing different query parameters. With our second approach we generate information landscapes, i.e. two-dimensional maps to display the results of search queries. In information landscapes documents with similar content are placed closer together, and islands appear where there is a concentration of closely related documents. The paper closes with a comparison of the two approaches and suggestion about when to use which approach.
  • Zeitschriftenartikel
    Mapping platforms into a new open science model for machine learning
    (it - Information Technology: Vol. 61, No. 4, 2019) Weißgerber, Thomas; Granitzer, Michael
    Data-centric disciplines like machine learning and data science have become major research areas within computer science and beyond. However, the development of research processes and tools did not keep pace with the rapid advancement of the disciplines, resulting in several insufficiently tackled challenges to attain reproducibility, replicability, and comparability of achieved results. In this discussion paper, we review existing tools, platforms and standardization efforts for addressing these challenges. As a common ground for our analysis, we develop an open science centred process model for machine learning research, which combines openness and transparency with the core processes of machine learning and data science. Based on the features of over 40 tools, platforms and standards, we list the, in our opinion, 11 most central platforms for the research process in this paper. We conclude that most platforms cover only parts of the requirements for overcoming the identified challenges.