On Approximate Range Mode and Range Selection
For any epsilon in (0,1), a (1+epsilon)-approximate range mode query asks for the position of an element whose frequency in the query range is at most a factor (1+epsilon) smaller than the true mode. For this problem, we design a data structure occupying O(n/epsilon) bits of space to answer queries in O(lg(1/epsilon)) time. This is an encoding data structure which does not require access to the input sequence; the space cost of this structure is asymptotically optimal for constant epsilon as we also prove a matching lower bound. Furthermore, our solution improves the previous best result of Greve et al. (Cell Probe Lower Bounds and Approximations for Range Mode, ICALP'10) by saving the space cost by a factor of lg n while achieving the same query time. In dynamic settings, we design an O(n)-word data structure that answers queries in O(lg n /lg lg n) time and supports insertions and deletions in O(lg n) time, for any constant epsilon in (0,1); the bounds for non-constant epsilon = o(1) are also given in the paper. This is the first result on dynamic approximate range mode; it can also be used to obtain the first static data structure for approximate 3-sided range mode queries in two dimensions.
Another problem we consider is approximate range selection. For any alpha in (0,1/2), an alpha-approximate range selection query asks for the position of an element whose rank in the query range is in [k - alpha s, k + alpha s], where k is a rank given by the query and s is the size of the query range. When alpha is a constant, we design an O(n)-bit encoding data structure that can answer queries in constant time and prove this space cost is asymptotically optimal. The previous best result by Krizanc et al. (Range Mode and Range Median Queries on Lists and Trees, Nordic Journal of Computing, 2005) uses O(n lg n) bits, or O(n) words, to achieve constant approximation for range median only. Thus we not only improve the space cost, but also provide support for any arbitrary k given at query time. We also analyse our solutions for non-constant alpha.
data structures
approximate range query
range mode
range median
Theory of computation~Data structures design and analysis
57:1-57:14
Regular Paper
Hicham
El-Zein
Hicham El-Zein
Cheriton School of Computer Science, University of Waterloo, Canada
Meng
He
Meng He
Faculty of Computer Science, Dalhousie University, Canada
J. Ian
Munro
J. Ian Munro
Cheriton School of Computer Science, University of Waterloo, Canada
Yakov
Nekrich
Yakov Nekrich
Department of Computer Science, Michigan Technological University, USA
Bryce
Sandlund
Bryce Sandlund
Cheriton School of Computer Science, University of Waterloo, Canada
10.4230/LIPIcs.ISAAC.2019.57
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Hicham El-Zein, Meng He, J. Ian Munro, Yakov Nekrich, and Bryce Sandlund
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