121 research outputs found
Map Matching with Simplicity Constraints
We study a map matching problem, the task of finding in an embedded graph a
path that has low distance to a given curve in R^2. The Fr\'echet distance is a
common measure for this problem. Efficient methods exist to compute the best
path according to this measure. However, these methods cannot guarantee that
the result is simple (i.e. it does not intersect itself) even if the given
curve is simple. In this paper, we prove that it is in fact NP-complete to
determine the existence a simple cycle in a planar straight-line embedding of a
graph that has at most a given Fr\'echet distance to a given simple closed
curve. We also consider the implications of our proof on some variants of the
problem
Experimental analysis of the accessibility of drawings with few segments
The visual complexity of a graph drawing is defined as the number of
geometric objects needed to represent all its edges. In particular, one object
may represent multiple edges, e.g., one needs only one line segment to draw two
collinear incident edges. We study the question if drawings with few segments
have a better aesthetic appeal and help the user to asses the underlying graph.
We design an experiment that investigates two different graph types (trees and
sparse graphs), three different layout algorithms for trees, and two different
layout algorithms for sparse graphs. We asked the users to give an aesthetic
ranking on the layouts and to perform a furthest-pair or shortest-path task on
the drawings.Comment: Appears in the Proceedings of the 25th International Symposium on
Graph Drawing and Network Visualization (GD 2017
Locally Correct Frechet Matchings
The Frechet distance is a metric to compare two curves, which is based on
monotonous matchings between these curves. We call a matching that results in
the Frechet distance a Frechet matching. There are often many different Frechet
matchings and not all of these capture the similarity between the curves well.
We propose to restrict the set of Frechet matchings to "natural" matchings and
to this end introduce locally correct Frechet matchings. We prove that at least
one such matching exists for two polygonal curves and give an O(N^3 log N)
algorithm to compute it, where N is the total number of edges in both curves.
We also present an O(N^2) algorithm to compute a locally correct discrete
Frechet matching
Four Soviets Walk the Dog-Improved Bounds for Computing the Fr\'echet Distance
Given two polygonal curves in the plane, there are many ways to define a
notion of similarity between them. One popular measure is the Fr\'echet
distance. Since it was proposed by Alt and Godau in 1992, many variants and
extensions have been studied. Nonetheless, even more than 20 years later, the
original algorithm by Alt and Godau for computing the Fr\'echet
distance remains the state of the art (here, denotes the number of edges on
each curve). This has led Helmut Alt to conjecture that the associated decision
problem is 3SUM-hard.
In recent work, Agarwal et al. show how to break the quadratic barrier for
the discrete version of the Fr\'echet distance, where one considers sequences
of points instead of polygonal curves. Building on their work, we give a
randomized algorithm to compute the Fr\'echet distance between two polygonal
curves in time on a pointer machine
and in time on a word RAM. Furthermore, we show that
there exists an algebraic decision tree for the decision problem of depth
, for some . We believe that this
reveals an intriguing new aspect of this well-studied problem. Finally, we show
how to obtain the first subquadratic algorithm for computing the weak Fr\'echet
distance on a word RAM.Comment: 34 pages, 15 figures. A preliminary version appeared in SODA 201
A Framework for Algorithm Stability
We say that an algorithm is stable if small changes in the input result in
small changes in the output. This kind of algorithm stability is particularly
relevant when analyzing and visualizing time-varying data. Stability in general
plays an important role in a wide variety of areas, such as numerical analysis,
machine learning, and topology, but is poorly understood in the context of
(combinatorial) algorithms. In this paper we present a framework for analyzing
the stability of algorithms. We focus in particular on the tradeoff between the
stability of an algorithm and the quality of the solution it computes. Our
framework allows for three types of stability analysis with increasing degrees
of complexity: event stability, topological stability, and Lipschitz stability.
We demonstrate the use of our stability framework by applying it to kinetic
Euclidean minimum spanning trees
Stability analysis of kinetic orientation-based shape descriptors
We study three orientation-based shape descriptors on a set of continuously
moving points: the first principal component, the smallest oriented bounding
box and the thinnest strip. Each of these shape descriptors essentially defines
a cost capturing the quality of the descriptor and uses the orientation that
minimizes the cost. This optimal orientation may be very unstable as the points
are moving, which is undesirable in many practical scenarios. If we bound the
speed with which the orientation of the descriptor may change, this may lower
the quality of the resulting shape descriptor. In this paper we study the
trade-off between stability and quality of these shape descriptors. We first
show that there is no stateless algorithm, an algorithm that keeps no state
over time, that both approximates the minimum cost of a shape descriptor and
achieves continuous motion for the shape descriptor. On the other hand, if we
can use the previous state of the shape descriptor to compute the new state, we
can define "chasing" algorithms that attempt to follow the optimal orientation
with bounded speed. We show that, under mild conditions, chasing algorithms
with sufficient bounded speed approximate the optimal cost at all times for
oriented bounding boxes and strips. The analysis of such chasing algorithms is
challenging and has received little attention in literature, hence we believe
that our methods used in this analysis are of independent interest
Topological Stability of Kinetic -Centers
We study the -center problem in a kinetic setting: given a set of
continuously moving points in the plane, determine a set of (moving)
disks that cover at every time step, such that the disks are as small as
possible at any point in time. Whereas the optimal solution over time may
exhibit discontinuous changes, many practical applications require the solution
to be stable: the disks must move smoothly over time. Existing results on this
problem require the disks to move with a bounded speed, but this model is very
hard to work with. Hence, the results are limited and offer little theoretical
insight. Instead, we study the topological stability of -centers.
Topological stability was recently introduced and simply requires the solution
to change continuously, but may do so arbitrarily fast. We prove upper and
lower bounds on the ratio between the radii of an optimal but unstable solution
and the radii of a topologically stable solution---the topological stability
ratio---considering various metrics and various optimization criteria. For we provide tight bounds, and for small we can obtain nontrivial
lower and upper bounds. Finally, we provide an algorithm to compute the
topological stability ratio in polynomial time for constant
Coordinated Schematization for Visualizing Mobility Patterns on Networks
GPS trajectories of vehicles moving on a road network are a valuable source of traffic information. However, the sheer volume of available data makes it challenging to identify and visualize salient patterns. Meaningful visual summaries of trajectory collections require that both the trajectories and the underlying network are aggregated and simplified in a coherent manner. In this paper we propose a coordinated fully-automated pipeline for computing a schematic overview of mobility patterns from a collection of trajectories on a street network. Our pipeline utilizes well-known building blocks from GIS, automated cartography, and trajectory analysis: map matching, road selection, schematization, movement patterns, and metro-map style rendering. We showcase the results of our pipeline on two real-world trajectory collections around The Hague and Beijing
- …