Dr. rer. nat.
Now showing 1 - 10 of 17
Publicationtanh Neurons are Bayesian Decision Makers( 2021)
; ;The hyperbolic tangent (tanh) is a traditional choice for the activation function of the neurons of an artificial neural network. Here, we go through a simple calculation that shows that this modeling choice is linked to Bayesian decision theory. Our brief, tutorial-like discussion is intended as a reference to an observation rarely mentioned in standard textbooks.
PublicationMax-Sum Dispersion via Quantum Annealing( 2019)
; ; ;We devise an Ising model for the max-sum dispersion problem which occurs in contexts such as Web search or text summarization. Given this Ising model, max-sum dispersion can be solved on adiabatic quantum computers; in proof of concept simulations, we solve the corresponding Schrödinger equations and observe our approach to work well.
PublicationA QUBO Formulation of the k-Medoids Problem( 2019)
; ; ; ;We are concerned with k-medoids clustering and propose aquadratic unconstrained binary optimization (QUBO) formulation of the problem of identifying k medoids among n data points without having to cluster the data. Given our QUBO formulation of this NP-hard problem, it should be possible to solve it on adiabatic quantum computers.
PublicationDetecting Mobility Patterns with Stationary Bluetooth Sensors: A real-world Case Study( 2015)
;Müller, Marc ; ;A Bluetooth sensor network was built up in the city of Bonn to measure Bluetooth MAC-addresses. The results of the acquired data are separated on a macro level and mobility patterns. We have collected nearly 5 million data points from 14 distinct stationary sensors over a period of 1 month and recognized over 85.000 unique devices. We show that the data is sufficiently dense to detect commuter patterns based on a Fourier analysis. In addition, we discuss limitations found in the dataset and present lessons learned.
PublicationUsing Bluetooth to track mobility patterns: Depicting its potential based on various case studies( 2013)
;Ellersiek, Timothy ;Andrienko, Gennady ;Andrienko, Natalia ; ;Müller, MarcDuring the past years the interest in the exploitation of mobility information has increased significantly. A growing number of companies and research institutions are interested in the analysis of mobility data with demand of a high level of spatial detail. Means of tracking persons in our environment can nowadays be fulfilled by utilizing several technologies, for example the Bluetooth technology, offering means to obtain movement data. This paper gives an overview of four case studies in the field of Bluetooth tracking which were conducted in order to provide helpful insights on movement aspects for decision makers in their specific microcosm. Aim is to analyse spatio-temporal validity of Bluetooth tracking, and in doing so, to describe the potential of Bluetooth in pedestrian mobility mining.
PublicationAnalyzing temporal usage patterns of street segments based on GPS data - a case study in Switzerland( 2012)
;Schreinemacher, Jutta ; ;Bareth, GeorgMobility has become a key component of our social and economic activities. Depending on our activities, the usage of the road network varies, showing, for example, an increased traffic load in early morning when people go to work. Such usage patterns are of interest not only for traffic management but also for private companies offering location-based services. In this paper we analyze the temporal usage of street segments based on a large GPS data set in Switzerland. We first conduct a clustering analysis which detects groups of segments with similar temporal usage patterns. Afterwards we analyze the patterns with respect to their temporal and spatial characteristics. Our analysis shows that GPS data is qualified for an analysis of temporal usage patterns, identifying shopping and leisure activities.
PublicationAnalyse von raumzeitlichen Bewegungsmustern auf Basis von Bluetooth-Sensoren( 2012)
;Ellersiek, Timothy ; ;Informationen über das Kundenverhalten sind ein wesentlicher Forschungsbestandteil im Bereich des Marketings. Dabei hilft das Verstehen von Entscheidungsfindungsprozessendes potenziellen Kunden in einem raumzeitlichen Wechselspiel mit seiner Umgebung dem Anbieter die Qualität seines Produktes aufzuwerten. Bis vor kurzem wurden Veränderungen am Produkt häufig durch Trial-and-Error-Methoden vorgenommen; heute jedoch erlauben neuartige Technologien wie beispielsweise Bluetooth, GPS oder Video neue Möglichkeiten, dem Anbieter zielgenaue Qualitäts-Validierungen seiner Produkte durchzuführen. In dem vorliegenden Beispiel wird der vorangehende Sachverhalt auf die Mobilität im Duisburger Zoo übertragen. Repräsentative Daten über das Bewegungsverhalten der Besucher werden unter Wahrung der Privatsphäre in einem siebentägigen Versuchszeitraum anhand von Bluetooth-Tracking erfasst, anschließend aufbereitet, analysiert und interpretiert. Die Ergebnisse zeigen, dass die Reproduktion von Mobilitätsinformationendurch die Erfassung von privaten Endgeräten der Besucher mittels einer kostengünstigen Technologie wie das Bluetooth möglich ist.
PublicationRobustness analyses for repeated mobility surveys in outdoor advertisingA growing number of companies use mobility data in their day-to-day business. However, as the data grows older, new data has to be collected in order to keep applications up-to-date. Consequently, it is of great importance to know the impact that a different mobility sample may cause. This aspect of analysis has been largely neglected in mobility data mining research so far. In this paper we therefore analyze the robustness of performance measures with respect to a changed GPS sample in outdoor advertisement. The evaluation of outdoor advertising campaigns is a challenging application because it requires the evaluation of mobility data on a very fine spatial level. Thus, the application has a higher dependency on routes of individual test persons than classical mobility surveys. In our rob ustness analysis we apply bootstrapping and subsampling in order to measure the effect of a) a repeated mobility survey and b) a mobility survey of smaller size. We conduct our experiments on a real-world data set from Swiss outdoor advertising. Our results show that the effect is comparably small for a typical campaign and may be mitigated further by increasing the campaign size.
PublicationModeling micro-movement variability in mobility studies( 2011)
; ; ; ;During the past years the interest in the exploitation of mobility information has increased significantly. Along with these interests, new demands on mobility data sets have been posed. One particular demand is the evaluation of movement data on a high level of spatial detail. The high dimensionality of geographic space, however, makes this requirement hard to fulfill. Even large mobility studies cannot guarantee to comprise all movement variation on a high level of detail. In this paper we present an approach to increase the variability of movement data on microscopic scale in order to achieve a better representation of population movement. Our approach consists of two steps. First, we perform a spatial aggregation of trajectory data in order to counteract sparseness and to preserve movement on macroscopic scale. Second, we disaggregate the data in geographic space based on traffic distribution knowledge using repeated simulation. Our approach is applied in a real-world business application for the Ger-man outdoor advertising industry to measure the performance of poster sites.
PublicationChallenges and advantages of using GPS data in outdoor advertisementA growing number of companies use mobility data in their day-to-day business. Especially in the area of outdoor advertising, GPS devices have been successfully applied in order to measure poster performance in recent years. Based on personal mobility traces, the quality and precision of performance measures has increased significantly. However, the usage of GPS technology poses several challenges when applied to critical business processes. We will present several challenges and solutions which we developed in the last years of our mobility research with GPS data.