971 research outputs found
Orthogonal Range Reporting and Rectangle Stabbing for Fat Rectangles
In this paper we study two geometric data structure problems in the special
case when input objects or queries are fat rectangles. We show that in this
case a significant improvement compared to the general case can be achieved.
We describe data structures that answer two- and three-dimensional orthogonal
range reporting queries in the case when the query range is a \emph{fat}
rectangle. Our two-dimensional data structure uses words and supports
queries in time, where is the number of points in the
data structure, is the size of the universe and is the number of points
in the query range. Our three-dimensional data structure needs
words of space and answers queries in time. We also consider the rectangle stabbing problem on a set of
three-dimensional fat rectangles. Our data structure uses space and
answers stabbing queries in time.Comment: extended version of a WADS'19 pape
Categorical Range Reporting with Frequencies
In this paper, we consider a variant of the color range reporting problem called color reporting with frequencies. Our goal is to pre-process a set of colored points into a data structure, so that given a query range Q, we can report all colors that appear in Q, along with their respective frequencies. In other words, for each reported color, we also output the number of times it occurs in Q. We describe an external-memory data structure that uses O(N(1+log^2D/log N)) words and answers one-dimensional queries in O(1 +K/B) I/Os, where N is the total number of points in the data structure, D is the total number of colors in the data structure, K is the number of reported colors, and B is the block size.
Next we turn to an approximate version of this problem: report all colors sigma that appear in the query range; for every reported color, we provide a constant-factor approximation on its frequency. We consider color reporting with approximate frequencies in two dimensions. Our data structure uses O(N) space and answers two-dimensional queries in O(log_B N +log^*B + K/B) I/Os in the special case when the query range is bounded on two sides. As a corollary, we can also answer one-dimensional approximate queries within the same time and space bounds
Orthogonal Point Location and Rectangle Stabbing Queries in 3-d
In this work, we present a collection of new results on two fundamental problems in geometric data structures: orthogonal point location and rectangle stabbing.
- Orthogonal point location. We give the first linear-space data structure that supports 3-d point location queries on n disjoint axis-aligned boxes with optimal O(log n) query time in the (arithmetic) pointer machine model. This improves the previous O(log^{3/2} n) bound of Rahul [SODA 2015]. We similarly obtain the first linear-space data structure in the I/O model with optimal query cost, and also the first linear-space data structure in the word RAM model with sub-logarithmic query time.
- Rectangle stabbing. We give the first linear-space data structure that supports 3-d 4-sided and 5-sided rectangle stabbing queries in optimal O(log_wn+k) time in the word RAM model. We similarly obtain the first optimal data structure for the closely related problem of 2-d top-k rectangle stabbing in the word RAM model, and also improved results for 3-d 6-sided rectangle stabbing.
For point location, our solution is simpler than previous methods, and is based on an interesting variant of the van Emde Boas recursion, applied in a round-robin fashion over the dimensions, combined with bit-packing techniques. For rectangle stabbing, our solution is a variant of Alstrup, Brodal, and Rauhe\u27s grid-based recursive technique (FOCS 2000), combined with a number of new ideas
Step into Computational Geometry Notebook III
Coordinated Science Laboratory was formerly known as Control Systems LaboratoryJoint Services Electronics Program / N00014-79-C-0424National Science FoundationControl Data Corporatio
Four-Dimensional Dominance Range Reporting in Linear Space
In this paper we study the four-dimensional dominance range reporting problem and present data structures with linear or almost-linear space usage. Our results can be also used to answer four-dimensional queries that are bounded on five sides. The first data structure presented in this paper uses linear space and answers queries in O(log^{1+?} n + k log^? n) time, where k is the number of reported points, n is the number of points in the data structure, and ? is an arbitrarily small positive constant. Our second data structure uses O(n log^? n) space and answers queries in O(log n+k) time.
These are the first data structures for this problem that use linear (resp. O(n log^? n)) space and answer queries in poly-logarithmic time. For comparison the fastest previously known linear-space or O(n log^? n)-space data structure supports queries in O(n^? + k) time (Bentley and Mauer, 1980). Our results can be generalized to d ? 4 dimensions. For example, we can answer d-dimensional dominance range reporting queries in O(log log n (log n/log log n)^{d-3} + k) time using O(n log^{d-4+?} n) space. Compared to the fastest previously known result (Chan, 2013), our data structure reduces the space usage by O(log n) without increasing the query time
4D Range Reporting in the Pointer Machine Model in Almost-Optimal Time
In the orthogonal range reporting problem we must pre-process a set of
multi-dimensional points, so that for any axis-parallel query rectangle all
points from can be reported efficiently. In this paper we study the
query complexity of multi-dimensional orthogonal range reporting in the pointer
machine model. We present a data structure that answers four-dimensional
orthogonal range reporting queries in almost-optimal time and uses space, where is the number of points in and
is the number of points in . This is the first data structure
with nearly-linear space usage that achieves almost-optimal query time in 4d.
This result can be immediately generalized to dimensions: we show that
there is a data structure supporting -dimensional range reporting queries in
time for any constant .Comment: Accepted for publication in SODA'2
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