Topology control

Information between two nodes in a network is sent based on the network topology, the structure of links connecting pairs of nodes of a network. The task of topology control is to choose a connecting subset from all possible links such that the overall network performance is good. For instance, a goal of topology control is to reduce the number of links to make routing on the topology faster and easier

Constrained free space diagrams: A tool for trajectory analysis

Time plays an important role in the analysis of moving object data. For many applications it is not sufficient to only compare objects at exactly the same times, or to consider only the geometry of their trajectories. We show how to leverage between these two approaches by extending a tool from curve analysis, namely the free space diagram. Our approach also allows us to take further attributes of the objects like speed or direction into account. We demonstrate the usefulness of the new tool by applying it to the problem of detecting single file movement. A single file is a set of moving entities, which are following each other, one behind the other. Our algorithm is the first one developed for detecting such movement patterns. For this application, we analyse demonstrate the performance of our tool both theoretically experimentally. Keywords: trajectories; movement patterns; single file movement; similarity measure

Delaunay triangulations in O(sort(n)) time and more

We present several results about Delaunay triangulations (DTs) and convex hulls in transdichotomous and hereditary settings: (i) the DT of a planar point set can be computed in expected time O(sort(n)) on a word RAM, where sort(n) is the time to sort n numbers. We assume that the word RAM supports the shuffle-operation in constant time; (ii) if we know the ordering of a planar point set in x- and in y-direction, its DT can be found by a randomized algebraic computation tree of expected linear depth; (iii) given a universe U of points in the plane, we construct a data structure D for Delaunay queries: for any P in U, D can find the DT of P in time O(|P|log log|U|); (iv) given a universe U of points in 3-space in general convex position, there is a data structure D for convex hull queries: for any P in U, D can find the convex hull of P in time O(|P|(log log|U|)2); (v) given a convex polytope in 3-space with n vertices which are colored with chi > 2 colors, we can split it into the convex hulls of the individual color classes in time O(n(log log n)2). The results (i)-(iii) generalize to higher dimensions. We need a wide range of techniques. Most prominently, we describe a reduction from DTs to nearest-neighbor graphs that relies on a new variant of randomized incremental constructions using dependent sampling

Four Soviets walk the dog, with an application to Alt's conjecture

Given two polygonal curves in the plane, there are several ways to define a measure of similarity between them. One measure that has been extremely popular in the past is the Frechet distance. Since it has been proposed by Alt and Godau in 1992, many variants and extensions have been described. However, even 20 years later, the original O(n^2 log n) algorithm by Alt and Godau for computing the Frechet distance remains the state of the art (here n denotes the number of vertices 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 Frechet distance, where we consider sequences of points instead of polygonal curves. Building on their work, we give an algorithm to compute the Frechet distance between two polygonal curves in time O(n^2 (log n)^(1/2) (\log\log n)^(3/2)) on a pointer machine and in time O(n^2 (loglog n)^2) on a word RAM. Furthermore, we show that there exists an algebraic decision tree for the Frechet problem of depth O(n^(2-epsilon)), for some epsilon > 0. This provides evidence that computing the Frechet distance may not be 3SUM-hard after all and reveals an intriguing new aspect of this well-studied problem

Four Soviets walk the dog: improved bounds for computing the Fréchet 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 Fréchet 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 O(n 2 logn) O(n2log⁡n) algorithm by Alt and Godau for computing the Fréchet distance remains the state of the art (here, n 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 Fréchet distance, where one considers sequences of points instead of polygonal curves. Building on their work, we give a randomized algorithm to compute the Fréchet distance between two polygonal curves in time O(n 2 logn − − − − √ (loglogn) 3/2 ) O(n2log⁡n(log⁡log⁡n)3/2) on a pointer machine and in time O(n 2 (loglogn) 2 ) O(n2(log⁡log⁡n)2) on a word RAM. Furthermore, we show that there exists an algebraic decision tree for the decision problem of depth O(n 2−ε ) O(n2−ε) , for some ε>0 ε>0 . 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 Fréchet distance on a word RAM

Finding long and similar parts of trajectories

A natural time-dependent similarity measure for two trajectories is their average distance at corresponding times. We give algorithms for computing the most similar subtrajectories under this measure, assuming the two trajectories are given as two polygonal, possibly self-intersecting lines with time stamps. For the case when a minimum duration of the subtrajectories is specified and the subtrajectories must start at corresponding times, we give a linear-time algorithm. The algorithm is based on a result of independent interest: We present a linear-time algorithm to find, for a piece-wise monotone function, an interval of at least a given length that has minimum average value. In the case that the subtrajectories may start at non-corresponding times, it appears difficult to give exact algorithms, even if the duration of the subtrajectories is fixed. For this case, we give (1+e)-approximation algorithms, for both fixed duration and when only a minimum duration is specified. Keywords: Trajectory analysis; Similarity measure; Moving object

Locally correct Fréchet matchings

\u3cp\u3eThe Fréchet distance is a metric to compare two curves, which is based on monotone matchings between these curves. We call a matching that results in the Fréchet distance a Fréchet matching. There are often many different Fréchet matchings and not all of these capture the similarity between the curves well. We propose to restrict the set of Fréchet matchings to “natural” matchings and to this end introduce locally correct Fréchet matchings. We prove that at least one such matching exists for two polygonal curves and give an O(N\u3csup\u3e3\u3c/sup\u3elog⁡N) algorithm to compute it, where N is the number of edges in both curves. We also present an O(N\u3csup\u3e2\u3c/sup\u3e) algorithm to compute a locally correct discrete Fréchet matching.\u3c/p\u3

Locally correct Fréchet 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

Compact flow diagrams for state sequences

\u3cp\u3eWe introduce the concept of using a flow diagram to compactly represent the segmentation of a large number of state sequences according to a set of criteria. We argue that this flow diagram representation gives an intuitive summary that allows the user to detect patterns within the segmentations. In essence, our aim is to generate a flow diagram with a minimum number of nodes that models a segmentation of the states in the input sequences. For a small number of state sequences we present efficient algorithms to compute aminimal flow diagram. For a large number of state sequences, we show that it is unlikely that efficient algorithms exist. Specifically, the problem is W[1]-hard if the number of state sequences is taken as a parameter. We introduce several heuristics for this problem.We argue about the usefulness of the flow diagram by applying the algorithms to two problems in sports analysis, and evaluate the performance of our algorithms on a football dataset and synthetic data.\u3c/p\u3

Voronoi diagram of polygonal chains under the discrete Fréchet distance

Polygonal chains are fundamental objects in many applications like pattern recognition and protein structure alignment. A well-known measure to characterize the similarity of two polygonal chains is the (continuous/discrete) Fréchet distance. In this paper, for the first time, we consider the Voronoi diagram of polygonal chains in d-dimension under the discrete Fréchet distance. Given a set C of n polygonal chains in d-dimension, each with at most k vertices, we prove fundamental properties of such a Voronoi diagram VD F (C). Our main results are summarized as follows.The combinatorial complexity of VD F(C) is at most O(n dk + ε).The combinatorial complexity of VD F(C) is at least Ω(n dk ) for dimension d = 1,2; and Ω(n d(k − 1) + 2) for dimension d > 2