Browsing by Author "Funke, Stefan (Prof. Dr.-Ing.)"

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Open Access
Algorithm engineering in geometric network planning and data mining
(2018) Seybold, Martin P.; Funke, Stefan (Prof. Dr.-Ing.)
The geometric nature of computational problems provides a rich source of solution strategies as well as complicating obstacles. This thesis considers three problems in the context of geometric network planning, data mining and spherical geometry. Geometric Network Planning: In the d-dimensional Generalized Minimum Manhattan Network problem (d-GMMN) one is interested in finding a minimum cost rectilinear network N connecting a given set of n pairs of points in ℝ^d such that each pair is connected in N via a shortest Manhattan path. The decision version of this optimization problem is known to be NP-hard. The best known upper bound is an O(log^{d+1} n) approximation for d>2 and an O(log n) approximation for 2-GMMN. In this work we provide some more insight in, whether the problem admits constant factor approximations in polynomial time. We develop two new algorithms, a `scale-diversity aware' algorithm with an O(D) approximation guarantee for 2-GMMN. Here D is a measure for the different `scales' that appear in the input, D ∈ O(log n) but potentially much smaller, depending on the problem instance. The other algorithm is based on a primal-dual scheme solving a more general, combinatorial problem - which we call Path Cover. On 2-GMMN it performs well in practice with good a posteriori, instance-based approximation guarantees. Furthermore, it can be extended to deal with obstacle avoiding requirements. We show that the Path Cover problem is at least as hard to approximate as the Hitting Set problem. Moreover, we show that solutions of the primal-dual algorithm are 4ω^2 approximations, where ω ≤ n denotes the maximum overlap of a problem instance. This implies that a potential proof of O(1)-inapproximability for 2-GMMN requires gadgets of many different scales and non-constant overlap in the construction. Geometric Map Matching for Heterogeneous Data: For a given sequence of location measurements, the goal of the geometric map matching is to compute a sequence of movements along edges of a spatially embedded graph which provides a `good explanation' for the measurements. The problem gets challenging as real world data, like traces or graphs from the OpenStreetMap project, does not exhibit homogeneous data quality. Graph details and errors vary in areas and each trace has changing noise and precision. Hence, formalizing what a `good explanation' is becomes quite difficult. We propose a novel map matching approach, which locally adapts to the data quality by constructing what we call dominance decompositions. While our approach is computationally more expensive than previous approaches, our experiments show that it allows for high quality map matching, even in presence of highly variable data quality without parameter tuning. Rational Points on the Unit Spheres: Each non-zero point in ℝ^d identifies a closest point x on the unit sphere S^{d-1}. We are interested in computing an ε-approximation y ∈ ℚ^d for x, that is exactly on S^{d-1} and has low bit-size. We revise lower bounds on rational approximations and provide explicit spherical instances. We prove that floating-point numbers can only provide trivial solutions to the sphere equation in ℝ^2 and ℝ^3. However, we show how to construct a rational point with denominators of at most 10(d-1)/ε^2 for any given ε ∈ (0, 1/8], improving on a previous result. The method further benefits from algorithms for simultaneous Diophantine approximation. Our open-source implementation and experiments demonstrate the practicality of our approach in the context of massive data sets, geo-referenced by latitude and longitude values.
Open Access
Improved algorithms for map rendering and route planning
(2020) Mendel, Thomas; Funke, Stefan (Prof. Dr.-Ing.)
In dieser Arbeit werden verschiedene Teilbereiche der Kartendarstellung und der Wegefindung betrachtet. Der erste Teil beschäftigt sich mit Grenzvereinfachung und Gebietsbeschriftung. Der zweite Teil stellt ein beschleunigtes Verfahren zur Berechnung kürzester Wege vor.