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DOI: 10.4230/LIPIcs.ESA.2017.65
K-Dominance in Multidimensional Data: Theory and Applications

Authors: Thomas Schibler and Subhash Suri

Published in: LIPIcs, Volume 87, 25th Annual European Symposium on Algorithms (ESA 2017)

Abstract
We study the problem of k-dominance in a set of d-dimensional vectors, prove bounds on the number of maxima (skyline vectors), under both worst-case and average-case models, perform experimental evaluation using synthetic and real-world data, and explore an application of k-dominant skyline for extracting a small set of top-ranked vectors in high dimensions where the full skylines can be unmanageably large.
Cite as

Thomas Schibler and Subhash Suri. K-Dominance in Multidimensional Data: Theory and Applications. In 25th Annual European Symposium on Algorithms (ESA 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 87, pp. 65:1-65:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{schibler_et_al:LIPIcs.ESA.2017.65,
  author =	{Schibler, Thomas and Suri, Subhash},
  title =	{{K-Dominance in Multidimensional Data: Theory and Applications}},
  booktitle =	{25th Annual European Symposium on Algorithms (ESA 2017)},
  pages =	{65:1--65:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-049-1},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{87},
  editor =	{Pruhs, Kirk and Sohler, Christian},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2017.65},
  URN =		{urn:nbn:de:0030-drops-78402},
  doi =		{10.4230/LIPIcs.ESA.2017.65},
  annote =	{Keywords: Dominance, skyline, database search, average case analysis, random vectors}
}
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