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Resilient k-d trees: k-means in space revisited

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Abstract

We propose a k-d tree variant that is resilient to a pre-described number of memory corruptions while still using only linear space. While the data structure is of independent interest, we demonstrate its use in the context of high-radiation environments. Our experimental evaluation demonstrates that the resulting approach leads to a significantly higher resiliency rate compared to previous results. This is especially the case for large-scale multi-spectral satellite data, which renders the proposed approach well-suited to operate aboard today’s satellites.

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Authors and Affiliations

  1. Department für Informatik, Carl von Ossietzky Universität Oldenburg, Oldenburg, 26111, Germany

    Fabian Gieseke

  2. Department of Computer Science, Goethe Universität Frankfurt am Main, Frankfurt am Main, 60325, Germany

    Gabriel Moruz

  3. Fakultät für Informatik, Technische Universität Dortmund, Dortmund, 44227, Germany

    Jan Vahrenhold

Authors
  1. Fabian Gieseke
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  2. Gabriel Moruz
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  3. Jan Vahrenhold
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Corresponding author

Correspondence to Fabian Gieseke.

Additional information

Fabian Gieseke received his Diplomas in Mathematics and Computer Science in 2006 fromthe University of Münster, Germany. He is currently working towards his PhD in the field of machine learning. His research interests include various related topics including support vector machines and its extensions to semi- and unsupervised learning settings as well as applications of machine learning techniques to problems in the field of astronomy.

Gabriel Moruz received his PhD in Computer Science from the University of Aarhus in 2007. Since 2007 he has been a post doctoral researcher in the group of Prof. Ulrich Meyer (Chair for Algorithm Engineering) at the Goethe University, Frankfurt am Main. His primary research interest concerns the design of efficient algorithms and data structures in practice. This involves designing and implementing algorithms that are aware of various hardware issues that have a great impact in practice. These include cacheaware and cache-oblivious algorithms, streaming algorithms, algorithms performing few branch mispredictions, as well as resilient algorithms, i.e., algorithms aware of memory corruption.

Jan Vahrenhold received his Diploma in Mathematics and his PhD and Habilitation in Computer Science from the University of Münster, Germany, in 1996, 1999, and 2004, respectively. Since 2006, he has been an Associate Professor with the Faculty of Computer Science, Technische Universität Dortmund, Germany. His current research interests include I/O-and cache-efficient algorithms and data structures, computational geometry, and computer science education.

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Gieseke, F., Moruz, G. & Vahrenhold, J. Resilient k-d trees: k-means in space revisited. Front. Comput. Sci. 6, 166–178 (2012). https://doi.org/10.1007/s11704-012-2870-8

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  • DOI: https://doi.org/10.1007/s11704-012-2870-8

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