Open Access

Stream processing is a popular paradigm to process huge amounts of unbounded data, which has gained significant attention in both academia and industry. Typical stream processing applications such as stock trading and network traffic monitoring require continuously analyzed results provided to end-users. During processing, the characteristics of data streams such as volume or velocity can vary, e.g., peak load or bursty streams can occur at certain points. In order to cope with such situations, it requires the analytical systems to be able to adapt the execution of stream processing as quickly as possible. In literature, different approaches adapting data stream processing such as load-shedding and elastic parallelization do exist. However, each of them have their different shortcomings like skewed results (due to the dropped data) or strong limits on the adaptation due to the parallelization overhead. One specific challenge motivating us is to minimize the impact of runtime adaptation on the overall data processing, in particular for real-time data analytics. Moreover, while the need to create adaptive stream processing systems is well known, there is currently no systematic and broad analysis of the solution range of creating adaptation mechanisms for stream processing applications. In this dissertation, we focus on algorithm switching as a fundamental approach to the construction of adaptive stream processing systems. Algorithm switching is a form of adaptation, where stream processing algorithms, with fundamentally similar input-/output-characteristics but different runtime tradeoffs like resource consumption or precision, are replaced to optimize the processing. As our overall goal, we present a general algorithm switching framework that models a wide range of switching solutions (called switch variants) in a systematic and reusable manner as well as characterizes the switch variants with their quality guarantees. Concretely, we focus on developing a general model of algorithm switching to systematically capture possible variants of different switching behavior. We also present a theoretical specification to predict the timeliness-related qualities for the switch variants. Moreover, from the practical perspective, we also develop a component-based design to ease the realization effort of the algorithm switching variants. Finally, we provide a validation of the algorithm switching framework against the realized switch variants.

In der hier vorgelegten Promotionsarbeit wird das Potenzial gruppenbasierter und semistrukturierter Aushandlungsprozesse analysiert. In einer entsprechenden Interventionsstudie mit Pre- und Post-Analysen wurden 146 Schüler:innen einer Gesamtschule in Niedersachsen/Deutschland aufgefordert, Begründungen zu acht selbstentwickelten Argumenten zu einem Thema über den Erhalt der lokalen Biodiversität, einem bioethischen Konflikt im Rahmen von nachhaltiger Entwicklung, vor und nach einer gruppenbasierten Aushandlung zu formulieren und diese zu gewichten. Zu diesem Zweck verwendeten die Schüler:innen in allen Phasen die Zielmat als ein Instrument zur Strukturierung des Bewertungsprozesses. Die Begründungen wurden inhaltsanalytisch hinsichtlich der Nutzung argumentativer Ressourcen analysiert. Darüber hinaus wurde die Richtung der Veränderungen der Begründungen nach der Aushandlung qualitativ verglichen und die Veränderung der Gewichtungen quantitativ berechnet. Bei diesen Analysen wurden individuelle Gewichtungen und Begründungen beider Phasen und die Veränderung der Gewichtungen mit den Gruppengewichtungen verglichen. Die Ergebnisse der Begründungen zeigen, dass die Schüler:innen bereits vor dem Aushandlungsprozess über eine Bandbreite an argumentativen Ressourcen (nämlichen faktenbasierte und normative Ressourcen) verfügen. Die Ergebnisse des Vergleichs der Begründungen von der Pre- zur Post-Phase zeigen, dass etwa ein Drittel aller Begründungen verändert wurden. Die Richtung der Veränderung ist zudem sehr divers, da die Schüler:innen die Begründungen widerlegten, revidierten, aber auch bestätigten und verstärkten. Ebenso wurde etwa ein Drittel aller Gewichtungen in der Post-Phase verändert. Ein Vergleich der Gewichtungsänderung der Pre- zu Post-Phase mit der Gruppengewichtung zeigt, dass diese der Tendenz der Gruppengewichtung entspricht. Die Ergebnisse dieser Studie machen auf das Potenzial gruppenbasierter Aushandlungsprozesse in bioethischen Konflikten aufmerksam, nämlich die Aktivierung relevanter argumentativer Ressourcen und die Initiierung tiefer und revidierender Denkprozesse. Darüber hinaus zeigen die Daten das besondere Potenzial der in der Studie verwendeten Zielmat, nämlich die Unterstützung komplexer und sonst für Schüler:innen überfordernder kompensatorischer Gewichtungsstrategien.

Finding an available parking spot in city centers can be a cumbersome task for individual drivers and also negatively affects general traffic flow and CO2 emissions. In the context of smart cities and the internet of things this problem can be mitigated by using available data to monitor and predict parking occupancy in order to guide users to an available parking location near their destination. With this goal in mind there arise multiple challenges of which we introduce selected ones to propose novel solutions based on machine learning. The focus of this work is to enable the usage of readily available and inexpensive data sources like parking meter transactions, opposed to expensive technology like in-ground sensors or cameras where the costs prevent a widespread coverage. Our proposed data sources do not directly monitor the actual parking availability but still provide enough signal for our algorithms to infer the real parking situation with high accuracy. As part of this work we developed a parking availability prediction system based on parking meter transactions that was deployed to 33 german cities. A main contribution of our work is the proposal of a novel way to generate labels based on the parking transactions and to use semi-supervised-, more specifically positive-unlabeled learning, to leverage the sparse signal in order to require as little data as possible. Additionally, we utilize and design novel methodologies in the area of transfer learning to learn simultaneously from different cities which leads to the previously seldom explored setting of combining transfer learning with positive-unlabeled learning. We therefore introduce a novel algorithm to tackle this problem type. We hope that our work enables the deployment of smart parking systems at lower costs and therefore leads towards the goal of smart parking guidance in smart cities.

Recent decades have seen exponential growth in data acquisition attributed to advancements in edge device technology. Factory controllers, smart home appliances, mobile devices, medical equipment, and automotive sensors are a few examples of edge devices capable of collecting data. Traditionally, these devices are limited to data collection and transfer functionalities, whereas decision-making capabilities were missing. However, with the advancement in microcontroller and processor technologies, edge devices can perform complex tasks. As a result, it provides avenues for pushing training machine learning models to the edge devices, also known as learning-at-the-edge. Furthermore, these devices operate in a distributed environment that is constrained by high latency, slow connectivity, privacy, and sometimes time-critical applications. The traditional distributed machine learning methods are designed to operate in a centralized manner, assuming data is stored on cloud storage. The operating environment of edge devices is impractical for transferring data to cloud storage, rendering centralized approaches impractical for training machine learning models on edge devices. Decentralized Machine Learning techniques are designed to enable learning-at-the-edge without requiring data to leave the edge device. The main principle in decentralized learning is to build consensus on a global model among distributed devices while keeping the communication requirements as low as possible. The consensus-building process requires averaging local models to reach a global model agreed upon by all workers. The exact averaging schemes are efficient in quickly reaching global consensus but are communication inefficient. Decentralized approaches employ in-exact averaging schemes that generally reduce communication by communicating in the immediate neighborhood. However, in-exact averaging introduces variance in each worker's local values, requiring extra iterations to reach a global solution. This thesis addresses the problem of learning-at-the-edge devices, which is generally referred to as decentralized machine learning or Edge Machine Learning. More specifically, we will focus on the Decentralized Parallel Stochastic Gradient Descent (DPSGD) learning algorithm, which can be formulated as a consensus-building process among distributed workers or fast linear iteration for decentralized model averaging. The consensus-building process in decentralized learning depends on the efficacy of in-exact averaging schemes, which have two main factors, i.e., convergence time and communication. Therefore, a good solution should keep communication as low as possible without sacrificing convergence time. An in-exact averaging solution consists of a connectivity structure (topology) between workers and weightage for each link. We formulate an optimization problem with the objective of finding an in-exact averaging solution that can achieve fast consensus (convergence time) among distributed workers keeping the communication cost low. Since direct optimization of the objective function is infeasible, a local search algorithm guided by the objective function is proposed. Extensive empirical evaluations on image classification tasks show that the in-exact averaging solutions constructed through the proposed method outperform state-of-the-art solutions. Next, we investigate the problem of learning in a decentralized network of edge devices, where a subset of devices are close to each other in that subset but further apart from other devices not in the subset. Closeness specifically refers to geographical proximity or fast communication links. We proposed a hierarchical two-layer sparse communication topology that localizes dense communication among a subgroup of workers and builds consensus through a sparse inter-subgroup communication scheme. We also provide empirical evidence of the proposed solution scaling better on Machine Learning tasks than competing methods. Finally, we address scalability issues of a pairwise ranking algorithm that forms an important class of problem in online recommender systems. The existing solutions based on a parallel stochastic gradient descent algorithm define a static model parameter partitioning scheme, creating an imbalance of work distribution among distributed workers. We propose a dynamic block partitioning and exchange strategy for the model parameters resulting in work balance among distributed workers. Empirical evidence on publicly available benchmark datasets indicates that the proposed method scales better than the static block-based methods and outperforms competing state-of-the-art methods.

Road accidents are one of the leading causes of death worldwide, particularly among young people. The police and local authorities therefore strive to reduce the risk of accidents through appropriate road safety measures. In order to plan these measures, the relevant types of accidents, i. e., accidents with certain features, must first be recognized. However, the variety of accident features and the amount of resulting feature combinations make it impossible to monitor all accident types manually. In this thesis, methods are proposed to automatically identify interesting accident types. Here, it is investigated whether combinations of features occur together and how the temporal pattern of the combined occurrence behaves. The change mining approach can then be used to determine whether structural changes in frequency occur during the period under consideration. For example, a feature combination that suddenly appears more frequently or exhibits a change in seasonality should be prioritized for further investigation so that appropriate road safety measures may be initiated for that combination. The implemented strategic, multi-stage data mining framework based on frequent itemset mining, time series clustering, forecasting methods, and a scoring process is able to detect interesting feature combinations. These are then processed on a map in a web interface suitable for the respective audience in order to support the strategic planning of road safety measures. The framework is applied to several accident data sets from different countries to determine suitable default parameter values for the respective data analysis methods and to carefully align the methods. It is shown that there exist only minor dependencies of the parameter selection on the database to be analyzed. For operational planning, it is necessary to consider small geographic areas and identify the features that have the greatest impact on accident occurrence there. Therefore, the developed operational framework analyzes and predicts the course of accident time series, taking into account the associated feature-specific time series. On the one hand, this makes it possible to increase the forecast performance, and, on the other hand, to determine which accident features have a significant influence on the course of the accident numbers over time. The insights gained can be used as a basis for short-term measures.

Die vorliegende Dissertation möchte einen intuitiven Zugang zum Verständnis der allgemeinen Relativitätstheorie bieten. Dazu wird die gekrümmte Raumzeit eines rotierenden schwarzen Loches visualisiert, deren Geometrie durch die Kerr-Metrik beschrieben wird. Die Kerr-Raumzeit stellt eine rotationssymmetrische Vakuumlösung der Einsteinschen Feldgleichungen dar. In dieser Arbeit liegt ein besonderer Fokus auf dem Innenraum des schwarzen Loches, wie er durch die maximale analytische Erweiterung der Kerr-Raumzeit beschrieben wird. In den im Rahmen dieser Arbeit erstellten Videos wird die visuelle Wahrnehmung eines im Gravitationsfeld eines schwarzen Loches (in den meisten Fällen) frei fallenden Beobachters auf verschiedenen Bahnen um oder durch ein rotierendes schwarzes Loch wiedergegeben. Die Videos geben hierbei einen Überblick über alle möglichen Flugbahnen. Die Erstellung der Einzelbilder der Videos erfolgte mit Hilfe von Ray-Tracing. Bei diesem Verfahren werden die Bahnen der beim Beobachter eintreffenden Lichtstrahlen zurück bis zum Objekt berechnet. Die für die Berechnung der Videos notwendigen mathematischen Konzepte der zugrundeliegenden Theorie werden im ersten Teil der Arbeit ausführlich diskutiert und erläutert. Im zweiten Teil werden die Ergebnisse der Berechnungen in Form von Bildern und Videos präsentiert und ausführlich beschrieben.

Soil organic carbon (SOC) is the largest terrestrial carbon pool, holding a manifold of atmospheric carbon. The agricultural management of soils exerts a strong influence on this pool and thereby also on carbon fluxes between biosphere and atmosphere. Historically, land-use changes have caused large CO2 emissions from soils, which significantly contributed to global warming. The latter is expected to cause strong fluxes of CO2 from soils, due to enhanced microbial activity – the climate-carbon cycle feedback loop. This cumulative habilitation thesis focuses on the two major anthropogenic impacts on soil organic carbon as the largest terrestrial carbon pool: land use and global warming due to greenhouse gas emissions. Both have been steadily increasing with the expansion of human population and activity on the planet and are thus considered specific for the ‘Anthropocene’ as a synonym for the period in world history in which human activity leaves irreversible traces. Due to the importance of SOC and its management for climate change, but also for soil fertility and ecosystem resilience, it is crucial to understand i) which management options can maintain and increase SOC, ii) what are the mechanisms leading to SOC losses and stabilization, iii) how shall global net primary production (NPP) as a resource be used in a climate-smart and sustainable way, iv) how will global warming affect SOC dynamics. Together with several methodological aspects related to measuring, calculating and modelling SOC, the research presented in this thesis focuses on those four key questions.

Zusammenfassung Die vorliegende Arbeit untersucht Vorstellungen und Kompetenzen von Grund-, Haupt- und Realschul- (GHR-) Studierenden des Faches Mathematik zum geometrischen Symmetrie- und Abbildungsbegriff. Zu Beginn der Arbeit werden zentrale Begrifflichkeiten (Professionswissen, Kompetenzbegriff, Symmetrie- und Abbildungsbegriff, Vorstellungen) geklärt und der aktuelle Forschungsstand zum Themengebiet aufgezeigt. Diese Darstellungen dienen als Grundlage, um darauf aufbauend Vorstellungen zum geometrischen Symmetriebegriff herauszuarbeiten. Unter Vorstellungen zu einem Begriff werden in der Arbeit Deutungsmöglichkeiten verstanden. Im Gegensatz zu anderen mathematischen Bereichen, wie der Arithmetik und Analysis, finden sich in der Geometrie hierzu kaum Forschungsarbeiten. Zur Identifikation von Vorstellungen wurde ein qualitatives Vorgehen, vor allem in Form einer Literaturanalyse, gewählt. Dabei wurden folgende Vorstellungen herausgearbeitet: * Symmetrie als Regelmäßigkeit * Symmetrie als mathematische Bewegung * Symmetrie als physikalische Bewegung * Symmetrie als Zerlegung Basierend auf den identifizierten Vorstellungen schließt sich eine quantitativ- als auch qualitativ-empirische Untersuchung an, bei der unter Verwendung eines hierfür entwickelten, theoriebasierten Fragebogens zum einen genutzte Vorstellungen beim Umgang mit geometrischen Symmetrien und zum anderen Kompetenzen bezüglich des geometrischen Symmetrie- und Abbildungsbegriffs von GHR-Studierenden Untersuchungsinteresse sind. Die Arbeit schließt mit einer Zusammenfassung, einer Reflexion der vorgenommenen Untersuchungen und einem Ausblick mit offenen Forschungsfragen zum Themengebiet. Außerdem werden, ausgehend von den Untersuchungsergebnissen, didaktische Konsequenzen für den Mathematikunterricht sowie die universitäre Ausbildung von angehenden GHR-Lehrpersonen des Faches Mathematik aufgezeigt und diskutiert.

The study of vegetation-plot data on a broad geographical scale is of increasing importance in vegetation science. It significantly contributes to the transnational characterisation of vegetation types as well as the better understanding of their large-scale patterns and to habitat typologies, which are important for decision-making processes in European nature conservation. I examined semi-natural, saline and brackish Baltic Sea grasslands which occur on sedimentary flats at the transition between land and sea. Their diverse vegetation is dependent on low intensity grazing (Dijkema 1990). This valuable part of the European cultural landscape (Küster 2004), which is recognized as Annex I priority habitat type (Natura 2000; European Commission 2013), underwent an overall decrease in quality and quantity within the last 150 years, which is frequently related to abandonment. Thus, the coastal grasslands of the Baltic Sea have been assessed as Endangered in the European Red List of Habitats (Janssen et al. 2016). Within this thesis I (i) developed a proposal to integrate vegetation data using non-standard scales into general vegetation analyses, (ii) characterised the vegetation of Baltic Sea grasslands on transnational level, (iii) regarded them from a North-west European perspective, (iv) discussed their nature conservation aspects on European scale, (v) investigated changes in their plant species composition and discussed its possible relation to cessation of grazing and (vi) formulated a monitoring concept important for management planning in nature conservation.

This thesis focuses on cosmic rays and Nature of Science (NOS). The first aim of this work is to investigate whether the variegated aspects of cosmic ray research -from its historical development to the science topics addressed herein- can be used for a teaching approach with and about NOS. The efficacy of the NOS based teaching has been highlighted in many studies, aimed at developing innovative and more effective teaching strategies. The fil rouge that we propose unwinds through cosmic ray research, that with its century long history appears to be the perfect topic for a study of and through NOS. The second aim of the work is to find out what knowledge the pupils and students have regarding the many aspects of NOS. To this end we have designed, executed, and analyzed the outcomes of a sample-based investigation carried out with pupils and students in Palermo (Italy), Tübingen and Hildesheim (Germany), and constructed around an open-ended questionnaire. The main goal is to study whether intrinsic differences between the German and Italian samples can be observed. The thesis is divided in three parts. In the first part we reconstruct the intricate history of cosmic ray research. First, we present the initial studies that proceeded the discovery of Viktor Hess in 1912, and then the pioneer years of research that unveiled the phenomenological and interpretational features of cosmic radiation. We then continue with the history of the mature phase of cosmic rays research focusing on the discovery and characterization of extensive air showers. In the second part of the thesis we first present the various aspects of NOS, including the operative definition adopted here and based on the 14 objectives proposed by McComas, Almazroa, and Clough. We then discuss the design, and execution of our sample-based investigation, finally we report in details the results of our analysis, performed with the MAXQDA software program. In the third part of the thesis the aspects of cosmic ray research, in its historical, technological and cultural developments, are observed through the lens of NOS. We therefore highlight aspects, moments, episodes of cosmic ray research that might elucidate and substantiate, with examples and occasions for discussions, the 14 statements of McComas and collaborators, and we suggest some didactic objectives and units, which can be developed by pupils and teachers

Skulls of 1,901 harbor seals from the North Sea were systematically investigated for dental, periodontal and cranial disorders as well as pathological changes of the temporomandibular joint (TMJ). Volumetric bone mineral density (vBMD) and microarchitecture of the mandible of a subsample of these specimens were analyzed in respect to age-related changes. Age at death of examined seals ranged from 1 week to 25 years. Most of the specimens were collected in 1988, when the population suffered from a phocine distemper virus epizootic. Therefore, it is assumed that, contrary to other museum collections, only little overrepresentation of pathological skeletal condition is present in the analyzed death sample. Age- and sex-related differences in the frequency and severity of pathological changes were observed in the dentition and the TMJ. Intravital tooth loss, tooth fracture and periapical lesions were recorded more frequently in male seals than in femals. Lesions consistent with temporomandibular joint osteoarthritis (TMJ-OA) also occurred more frequently in males, while lesion severity tended to be higher in female specimens. Severity of TMJ-OA lesions was positively correlated with age. Significant age-related changes in vBMD and several microarchitectural parameters were observed between individuals of the age classes “young juveniles” (0.5–10 months),“yearlings” (12–23 months),and “adults” (12–25 years),indicating an overall increase in cortical and trabecular area, cortical thickness as well as vBMD with age. For juvenile animals (≤ 23 months), positive correlations with age were observed for cortical area and thickness, trabecular separation, as well as vBMD. Negative correlations with age existed for trabecular number and thickness as well as for trabecular bone volume fraction (BV/TV) in the juveniles. The findings suggest a reduction in BV/TV with age,due to the bone trabeculae becoming thinner,less numerous and more widely spaced. This detailed knowledge of age-related changes in the structure and mineralization of bones is an important prerequisite for interpreting osseous changes in wild mammals caused by external factors, as such as exposure to environmental contaminants. The examination of large skeletal collections enables the observation also of rare pathological conditions. In the present investigation a case of anodontia,diagnosed as a manifestation of hypohidrotic ectodermal dysplasia was for the first time described in the harbor seal. In five juvenile Baltic grey seals,severe osteomyelitis of the jaws was described for the first time. The condition was attributed to disturbed dentin formation,presumably of genetic causation, in the affected individuals. The present study highlights the fact that systematic analyses of museum collections can provide important insights into the dental and skeletal pathology of wild mammals. These data can be used for reconstructing the health situation and living condition of past animal population.

Grazing animals alter natural processes by affecting ecosystems and at the same time fulfilling ecosystem functions, thus they are regarded as ecosystem engineers. Effects of grazing are mainly studied in managed systems, where grazing animals are restricted in their movement and thus limited to certain vegetation types. On the island of Asinara the grazing system is now, due to its history as agro-penitentiary, a natural grazing system with donkeys, horses, goats, mouflons and wild boars. This multitude of grazers poses a challenge for the Asinara National Park and its management. Therefore this dissertation takes an interdisciplinary approach to investigate grazing animals and their interrelations with different components of the island ecosystem to analyse their role on the island and evaluate their effects on the biodiversity. The composition and distribution patterns of the five grazing animal species have been investigated in the context of the land-cover types of the island ecosystem. In addition, the input on the vegetation through endozoochorous seed dispersal by donkeys and goats was analysed, and the impact of grazing animals on dung beetle assemblages was studied in three highly frequented vegetation units, taking into account the intensity of use by the grazing animals. The results derived from this work highlight the importance of studying grazing animals and their interrelations within an island ecosystem. Moreover, the insights given in this thesis concerning the interrelations of grazing animals with different components of the island should open up the view on grazers and their multifaceted effects on the biodiversity, thus leading to management implementations for a sound functioning of the island ecosystem as well as the conservation and maintenance of biodiversity.

Infolge des gesellschaftlichen Wandels haben sich die Aufgaben und auch ihre Prioritätensetzung in der Pflegekinderhilfe verändert. Bspw. stellen sich die Ansprüche an erzieherische Fähigkeiten über die Pflegepersonen verfügen sollen, umfangreicher dar als noch vor Jahrzehnten. Zudem war eine Zusammenarbeit mit den Geburtseltern bis weit in das 20. Jahrhundert nicht vorgesehen. Gleichzeitig sind die bestehenden Beschreibungen der Pflegefamilie bis heute unscharf und in ständiger Veränderung darüber, wie eine (Pflege-) Familie aussehen soll, was sie charakterisiert, welche Aufgaben, welche Rollen vergeben werden. Die Forschung hat bisher zu wenig zur Weiterentwicklung beigetragen. Angesichts dessen bestehen Widersprüche für derartige Ausprägungen, wenn die sich wandelnden und als Norm festgelegten Lebensweisen nicht mit den sie umgebenden Umwelten harmonieren. Ihre Auswirkungen treten verstärkt auf der operativen Ebene hervor. Das vorliegende Forschungsprojekt hat Erstgespräche zwischen Fachkräften für Pflegekinder und Bewerbenden um ein Pflegekind evaluiert. Die in der Untersuchung freigelegten Ambivalenzen zeigen auf, dass sich einerseits die Anforderungen an den Pflegeauftrag gewandelt haben und andererseits diese veränderten Ansprüche offenbar in der Praxis noch nicht zufriedenstellend gelöst wurden. Deren Auftreten hat aber Auswirkungen u.a. auf die Herstellung eines gemeinsamen Arbeitsbündnisses in einem ersten Gespräch zur Aufnahme eines Pflegekindes sowie anschließend auf das Alltagsgeschehen während eines Pflegearrangements. Die Ergebnisse dieses Forschungsprojekts können als Grundlage dienen, ein transparentes und an den Bedarfen von Pflegefamilien orientiertes Vorgehen zu generieren.

Für die Hintergrundbeleuchtung bei Anzeigegeräten werden zunehmend LEDs eingesetzt. LEDs zeichnen sich durch eine lange Lebensdauer, geringe Energieaufnahme und kompakte Bauweise aus. Die hohe Emission des blauen Lichts im Spektrum von LEDs kann den menschlichen Organismus negativ beeinflussen. Bei der vorliegenden Arbeit handelt es sich um die Konstruktion und Realisierung einer neuartigen LED-Hintergrundbeleuchtung unter Einsatz von LED-Clustern mit steuerbarem Farbspektrum. Das System wird über einen Micro-Controller gesteuert, der einen beliebig vorgegebenen tagesverlaufähnlichen Beleuchtungszustand unter Verwendung verschiedener Parameter wie Umgebungslicht, Tageszeit und Farb-wiedergabe aufrechterhält. Ziel ist es, die Blaulichtemission des Lichtspektrums präzise zu regeln. Die Untersuchungen auf dem eigens hierfür entwickelten Prüfstand haben ergeben, dass die Blaulichtemission einer LED-Hintergrundbeleuchtung durch ein gesteuertes Farb-spektrum gezielt kontrolliert werden kann. Ein positiver Nebeneffekt ist die Möglichkeit, mit dieser Apparatur die Farbtemperatur und die Farbwiedergabe im Farbspektrum zu beeinflussen. Die erzielten Resultate werden insgesamt als Bestätigung für die Ma߬nahmen zur Reduzierung der Blaulichtemission gewertet.

Automating machine learning by providing techniques that autonomously find the best algorithm, hyperparameter configuration and preprocessing is helpful for both researchers and practitioners. Therefore, it is not surprising that automated machine learning has become a very interesting field of research. Bayesian optimization has proven to be a very successful tool for automated machine learning. In the first part of the thesis we present different approaches to improve Bayesian optimization by means of transfer learning. We present three different ways of considering meta-knowledge in Bayesian optimization, i.e. search space pruning, initialization and transfer surrogate models. Finally, we present a general framework for Bayesian optimization combined with meta-learning and conduct a comparison among existing work on two different meta-data sets. A conclusion is that in particular the meta-target driven approaches provide better results. Choosing algorithm configurations based on the improvement on the meta-knowledge combined with the expected improvement yields best results. The second part of this thesis is more application-oriented. Bayesian optimization is applied to large data sets and used as a tool to participate in machine learning challenges. We compare its autonomous performance and its performance in combination with a human expert. At two ECML-PKDD Discovery Challenges, we are able to show that automated machine learning outperforms human machine learning experts. Finally, we present an approach that automates the process of creating an ensemble of several layers, different algorithms and hyperparameter configurations. These kinds of ensembles are jokingly called Frankenstein ensembles and proved their benefit on versatile data sets in many machine learning challenges. We compare our approach Automatic Frankensteining with the current state of the art for automated machine learning on 80 different data sets and can show that it outperforms them on the majority using the same training time. Furthermore, we compare Automatic Frankensteining on a large-scale data set to more than 3,500 machine learning expert teams and are able to outperform more than 3,000 of them within 12 CPU hours.

In this thesis we design and test Learning Analytics algorithms for personalized tasks' sequencing that suggests the next task to a student according to his/her specific needs. Our solution is based on a sequencing policy derived from the Vygotsky's Zone of Proximal Development (ZPD), which denes those tasks that are neither too easy not too dicult for the student. The sequencer, called Vygotsky Policy Sequencer (VPS), can identify tasks in the ZPD thanks to the information it receives from performance prediction algorithms able to estimate the knowledge of the student. Under this context we describe hereafter the thesis contributions. (1) A feasibility evaluation of domain independent Matrix Factorization applied in ITS for Performance Prediction. (2) An adaption and the related evaluation of a domain independent update for online learning Matrix Factorization in ITS. (3) A novel Matrix Factorization update method based on Kalman Filters approach. Two different updating functions are used: (a) a simple one considering the task just seen, and (b) one able to derive the skills' deficiency of the student. (4) A new method for offline testing of machine learning controlled sequencers by modeling simulated environment composed by a simulated students and tasks with continuous knowledge and score representation and different diffculty levels. (5) The design of a minimal invasive API for the lightweight integration of machine learning components in larger systems to minimize the risk of integration and the cost of expertise transfer. Profiting from all these contributions, the VPS was integrated in a commercial system and evaluated with 100 children over a month. The VPS showed comparable learning gains and perceived experience results with those of the ITS sequencer. Finally, thanks to its better modeling abilities, the students finish faster the assigned tasks.

Time series represent the most widely spread type of data, occurring in a myriad of application domains, ranging from physiological sensors up to astronomical light intensities. The classification of time-series is one of the most prominent challenges, which utilizes a recorded set of expert-labeled time-series, in order to automatically predict the label of future series without the need of an expert.The patterns of time-series are often shifted in time, have different scales, contain arbitrarily repeating patterns and exhibit local distortions/noise. In other cases, the differences among classes are attributed to small local segments, rather than the global structure. For those reasons, values corresponding to a particular time-stamp have different semantics on different time-series. We call this phenomena as intra-class variations. The lion's share of this thesis is composed of presenting new methods that can accurately classify time-series instances, by handling variations. The answer towards resolving the bottlenecks of intra-class variations relies on not using the time-series values as direct features. Instead, the approach of this thesis is to extract a set of features that, on one hand, represent all the variations of the data and, on the other hand, can boost classification accuracy. In other words, this thesis proposes a list of methods that addresses diverse aspects of intra-class variations. The first proposed approach is to generate new training instances, by transforming the support vectors of an SVM. The second approach decomposes time-series through a segment-wise convolutional factorization. The strategy involves learning a set of patterns and weights, whose product can approximate each sub-sequence of the time series. However, the main contribution of the thesis is the third approach, called shapelet learning, which utilizes the training labels during the learning process, i.e. the process is supervised. Since the features are learned on the training labels, there is a higher tendency of performing strongly in terms of predicting the testing labels. In addition, we present a fast alternative method for shapelet discovery. Our strategy is to prune segment candidates using a two step approach. First of all, we prune candidates based on their similarity towards previously considered candidates. Secondly, non-similar (hence diverse) candidates are selected only if the features they produce improve the classification results. The last two chapters of the thesis describes two methods that extract features from datasets having special characteristics. More concretely, we propose a classification method suited for series having missing values, as well as a method that extract features from time series having repetitive patterns.

Sara Dannemann & Nico Herrmann: Nachweis einer historischen Hohlweggallerie bei Alfeld/Leine (Südniedersachsen) anhand von Vermessungsergebnissen und bodengeographischen Feldaufnahmen Moritz Sandner, Jasmin Karaschewski, Jan-Philip Dieck & Nico Herrmann: Genese einer linearen Hohlform auf Carbonatgestein im nördlichen Hildesheimer Wald – unter besonderer Berücksichtigung der Ausprägung periglazialer Lagen und der holozänen Pedogenese Svenja Elfers & Sabine Panzer-Krause: Die Stadtentwicklung in Hildesheim im Zeichen des demographischen Wandels Lien Lammers, Judith Lübcke & Sabine Panzer-Krause: Gestaltung und Pflege von Grünanlagen in benachteiligten Stadtquartieren: Welchen Beitrag leisten Stadtteilnetzwerke?