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When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ESA.2017.65
URN: urn:nbn:de:0030-drops-78402
URL: http://drops.dagstuhl.de/opus/volltexte/2017/7840/
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Schibler, Thomas ; Suri, Subhash

K-Dominance in Multidimensional Data: Theory and Applications

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LIPIcs-ESA-2017-65.pdf (0.5 MB)


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.

BibTeX - Entry

@InProceedings{schibler_et_al:LIPIcs:2017:7840,
  author =	{Thomas Schibler and Subhash Suri},
  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 =	{Kirk Pruhs and Christian Sohler},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2017/7840},
  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}
}

Keywords: Dominance, skyline, database search, average case analysis, random vectors
Seminar: 25th Annual European Symposium on Algorithms (ESA 2017)
Issue Date: 2017
Date of publication: 31.08.2017


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