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<item>
  <id>05780474</id>
  <dt>a</dt>
  <an>05780474</an>
  <augroup>
    <au>Greve, Mark</au>
    <au>J{\o}rgensen, Allan Gr{\o}nlund</au>
    <au>Larsen, Kasper Dalgaard</au>
    <au>Truelsen, Jakob</au>
  </augroup>
  <ti>Cell probe lower bounds and approximations for range mode.</ti>
  <so>Abramsky, Samson (ed.) et al., Automata, languages and programming. 37th international colloquium, ICALP 2010, Bordeaux, France, July 6--10, 2010. Proceedings, Part I. Berlin: Springer (ISBN 978-3-642-14164-5/pbk). Lecture Notes in Computer Science 6198, 605-616 (2010).</so>
  <py>2010</py>
  <pu>Berlin: Springer</pu>
  <lagroup>
    <la>EN</la>
  </lagroup>
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  </ccgroup>
  <utgroup>
  </utgroup>
  <cigroup>
  </cigroup>
  <ligroup>
    <li>doi:10.1007/978-3-642-14165-2_51</li>
  </ligroup>
  <abgroup>
    <ab>Summary: The mode of a multiset of labels, is a label that occurs at least as often as any other label. The input to the range mode problem is an array $A$ of size $n$. A range query $[i,j]$ must return the mode of the subarray $A[i],A[i + 1],\dots ,A[j]$. We prove that any data structure that uses $S$ memory cells of $w$ bits needs $\Omega(\frac{{\log} n}{\log (Sw/n)})$ time to answer a range mode query. Secondly, we consider the related range $k$-frequency problem. The input to this problem is an array $A$ of size $n$, and a query $[i,j]$ must return whether there exists a label that occurs precisely $k$ times in the subarray $A[i],A[i + 1],\dots ,A[j]$. We show that for any constant $k > 1$, this problem is equivalent to 2D orthogonal rectangle stabbing, and that for $k = 1$ this is no harder than four-sided 3D orthogonal range emptiness. Finally, we consider approximate range mode queries. A $c$-approximate range mode query must return a label that occurs at least $1/c$ times that of the mode. We describe a linear space data structure that supports 3-approximate range mode queries in constant time, and a data structure that uses $O(\frac{n}{\varepsilon})$ space and supports $(1 + \epsilon )$-approximation queries in $O({\log} {\frac {1}{\varepsilon}})$ time.</ab>
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