38 research outputs found
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
On the complexity of range searching among curves
Modern tracking technology has made the collection of large numbers of
densely sampled trajectories of moving objects widely available. We consider a
fundamental problem encountered when analysing such data: Given polygonal
curves in , preprocess into a data structure that answers
queries with a query curve and radius for the curves of that
have \Frechet distance at most to .
We initiate a comprehensive analysis of the space/query-time trade-off for
this data structuring problem. Our lower bounds imply that any data structure
in the pointer model model that achieves query time, where is
the output size, has to use roughly space in
the worst case, even if queries are mere points (for the discrete \Frechet
distance) or line segments (for the continuous \Frechet distance). More
importantly, we show that more complex queries and input curves lead to
additional logarithmic factors in the lower bound. Roughly speaking, the number
of logarithmic factors added is linear in the number of edges added to the
query and input curve complexity. This means that the space/query time
trade-off worsens by an exponential factor of input and query complexity. This
behaviour addresses an open question in the range searching literature: whether
it is possible to avoid the additional logarithmic factors in the space and
query time of a multilevel partition tree. We answer this question negatively.
On the positive side, we show we can build data structures for the \Frechet
distance by using semialgebraic range searching. Our solution for the discrete
\Frechet distance is in line with the lower bound, as the number of levels in
the data structure is , where denotes the maximal number of vertices
of a curve. For the continuous \Frechet distance, the number of levels
increases to
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
Data Structure Lower Bounds for Document Indexing Problems
We study data structure problems related to document indexing and pattern
matching queries and our main contribution is to show that the pointer machine
model of computation can be extremely useful in proving high and unconditional
lower bounds that cannot be obtained in any other known model of computation
with the current techniques. Often our lower bounds match the known space-query
time trade-off curve and in fact for all the problems considered, there is a
very good and reasonable match between the our lower bounds and the known upper
bounds, at least for some choice of input parameters. The problems that we
consider are set intersection queries (both the reporting variant and the
semi-group counting variant), indexing a set of documents for two-pattern
queries, or forbidden- pattern queries, or queries with wild-cards, and
indexing an input set of gapped-patterns (or two-patterns) to find those
matching a document given at the query time.Comment: Full version of the conference version that appeared at ICALP 2016,
25 page
Algorithms and Data Structures for Geometric Intersection Query Problems
University of Minnesota Ph.D. dissertation. September 2017. Major: Computer Science. Advisor: Ravi Janardan. 1 computer file (PDF); xi, 126 pages.The focus of this thesis is the topic of geometric intersection queries (GIQ) which has been very well studied by the computational geometry community and the database community. In a GIQ problem, the user is not interested in the entire input geometric dataset, but only in a small subset of it and requests an informative summary of that small subset of data. Formally, the goal is to preprocess a set A of n geometric objects into a data structure so that given a query geometric object q, a certain aggregation function can be applied efficiently on the objects of A intersecting q. The classical aggregation functions studied in the literature are reporting or counting the objects of A intersecting q. In many applications, the same set A is queried several times, in which case one would like to answer a query faster by preprocessing A into a data structure. The goal is to organize the data into a data structure which occupies a small amount of space and yet responds to any user query in real-time. In this thesis the study of the GIQ problems was conducted from the point-of-view of a computational geometry researcher. Given a model of computation and a GIQ problem, what are the best possible upper bounds (resp., lower bounds) on the space and the query time that can be achieved by a data structure? Also, what is the relative hardness of various GIQ problems and aggregate functions. Here relative hardness means that given two GIQ problems A and B (or, two aggregate functions f(A, q) and g(A, q)), which of them can be answered faster by a computer (assuming data structures for both of them occupy asymptotically the same amount of space)? This thesis presents results which increase our understanding of the above questions. For many GIQ problems, data structures with optimal (or near-optimal) space and query time bounds have been achieved. The geometric settings studied are primarily orthogonal range searching where the input is points and the query is an axes-aligned rectangle, and the dual setting of rectangle stabbing where the input is a set of axes-aligned rectangles and the query is a point. The aggregation functions studied are primarily reporting, top-k, and approximate counting. Most of the data structures are built for the internal memory model (word-RAM or pointer machine model), but in some settings they are generic enough to be efficient in the I/O-model as well
Space Efficient Two-Dimensional Orthogonal Colored Range Counting
In the two-dimensional orthogonal colored range counting problem, we
preprocess a set, , of colored points on the plane, such that given an
orthogonal query rectangle, the number of distinct colors of the points
contained in this rectangle can be computed efficiently.
For this problem, we design three new solutions, and the bounds of each can
be expressed in some form of time-space tradeoff.
By setting appropriate parameter values for these solutions, we can achieve
new specific results with (the space are in words and is an
arbitrary constant in ):
** space and query time;
** space and query time;
** space and
query time;
** space and query time.
A known conditional lower bound to this problem based on Boolean matrix
multiplication gives some evidence on the difficulty of achieving near-linear
space solutions with query time better than by more than a
polylogarithmic factor using purely combinatorial approaches. Thus the time and
space bounds in all these results are efficient.
Previously, among solutions with similar query times, the most
space-efficient solution uses space to answer queries in
time (SIAM. J. Comp.~2008).
Thus the new results listed above all achieve improvements in space
efficiency, while all but the last result achieve speed-up in query time as
well.Comment: full version of an ESA 2021 pape
Lower Bounds for Semialgebraic Range Searching and Stabbing Problems
In the semialgebraic range searching problem, we are to preprocess points
in s.t. for any query range from a family of constant complexity
semialgebraic sets, all the points intersecting the range can be reported or
counted efficiently. When the ranges are composed of simplices, the problem can
be solved using space and with query time with and this trade-off is almost tight. Consequently, there exists
low space structures that use space with query
time and fast query structures that use space with
query time. However, for the general semialgebraic ranges, only low space
solutions are known, but the best solutions match the same trade-off curve as
the simplex queries. It has been conjectured that the same could be done for
the fast query case but this open problem has stayed unresolved.
Here, we disprove this conjecture. We give the first nontrivial lower bounds
for semilagebraic range searching and related problems. We show that any data
structure for reporting the points between two concentric circles with
query time must use space, meaning, for
, space must be used. We also study
the problem of reporting the points between two polynomials of form
where are given at the
query time. We show . So
for , we must use space. For
the dual semialgebraic stabbing problems, we show that in linear space, any
data structure that solves 2D ring stabbing must use query
time. This almost matches the linearization upper bound. For general
semialgebraic slab stabbing problems, again, we show an almost tight lower
bounds.Comment: Submitted to SoCG'21; this version: readjust the table and other
minor change