Space-Efficient Data Structures for Polyominoes and Bar Graphs
CoRR(2023)
摘要
We provide a compact data structure for representing polyominoes that
supports neighborhood and visibility queries. Neighborhood queries concern
reporting adjacent cells to a given cell, and visibility queries determine
whether a straight line can be drawn within the polyomino that connects two
specified cells. For an arbitrary small $\epsilon >0$, our data structure can
encode a polyomino with $n$ cells in $(3+\epsilon)n + o(n)$ bits while
supporting all queries in constant time. The space complexity can be improved
to $3n+o(n)$, while supporting neighborhood queries in $\mathcal{O}(1)$ and
visibility queries in $\mathcal{O}(t(n))$ for any arbitrary $t(n) \in
\omega(1)$. Previous attempts at enumerating polyominoes have indicated that at
least $2.00091n - o(n)$ bits are required to differentiate between distinct
polyominoes, which shows our data structure is compact.
In addition, we introduce a succinct data structure tailored for bar graphs,
a specific subclass of polyominoes resembling histograms. We demonstrate that a
bar graph comprising $n$ cells can be encoded using only $n + o(n)$ bits,
enabling constant-time query processing. Meanwhile, $n-1$ bits are necessary to
represent any bar graph, proving our data structure is succinct.
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