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Die Digitalisierung der gesetzlichen Formen (2022)
Schmitz, Moritz
Die gesetzlichen Formen bilden einen praxisrelevanten Teilbereich der Rechtswissenschaft, über den in der Vergangenheit wenig diskutiert wurde. Die Einführung der Textform und der elektronischen Form im Jahr 2001 und die Verabschiedung des DiRUG verdeutlichen, dass die Digitalisierung auch vor den Formen des deutschen Rechtssystems keinen Halt macht. In dieser Dissertation wird die Entwicklung der Formen im Zuge der Digitalisierung aufgearbeitet. Dafür werden die papiergebundenen Formen und die digitalen Formen nach demselben Schema und unter denselben inhaltlichen Gesichtspunkten untersucht und chronologisch dargestellt. Hierdurch werden die Gemeinsamkeiten und Unterschiede systematisch herausgearbeitet. Besonderer Fokus liegt dabei auf den Formwirkungen sowie der Wirkungsäquivalenz von digitalen Formen und ihrem jeweiligen papiergebundenen Pendant. Zudem werden die digitalen notariellen Formen im Sinne des DiRUG in das altbekannte System der gesetzlichen Formen eingeordnet. Nach den Untersuchungen ist festzuhalten, dass die elektronische Form mittlerweile hinreichend wirkungsäquivalent im Vergleich zur Schriftform ist, auch hinsichtlich der Warnwirkung, die vom deutschen Gesetzgeber bisher noch anders beurteilt wird. Die digitale öffentliche Beglaubigung und die digitale notarielle Beurkundung im Sinne des DiRUG sind ebenfalls hinreichend gleichwertig im Vergleich zu ihrem jeweiligen papiergebundenen Äquivalent. Aus dem Grund wird sich in dieser Dissertation dafür ausgesprochen, den Anwendungsbereich der digitalen Formen entsprechend ihrer Wirkungsäquivalenz in Zukunft zu erweitern. Insgesamt ist die Digitalisierung der gesetzlichen Formen begrüßenswert und ein Schritt in die richtige Richtung.
School Noise and its Effects on Student Teachers and Practicing Teachers (2022)
Tomek, Raphaela
Noise in schools is considered a massive stress factor for teachers because of various reasons and can therefore lead to performance deficits in the job, but also to physical and psychological impairments. Consequently, research on school noise and its effects is essential. The three studies presented in this paper examined the immediate effects of noise on student teachers and the collateral effects on practicing teachers. In the first and second study, two experiments were conducted to examine the effects of noise during breaks on stress experience, performance in a concentration test, and error correction of a dictation. Based on the transactional stress model (Lazarus & Folkman, 1984), it was hypothesized that noise leads to an increase in stress experience. According to the maximal adaptability theory (Hancock & Warm, 1989, 2003), noise should initially cause optimal performance, but in the long run should cause performance impairment. For this purpose, in the first study 74 and in the second study 104 student teachers of the University of Passau worked on two different concentration tests and corrected a student’s dictation while listening to short, continuous, or no noise. In both experiments, continuous noise led to an increase in the experience of stress. Neither short nor continuous noise led to a deterioration in concentration performance. Further, different findings emerged: In the first experiment, a short concentration test in combination with continuous noise led to positive effects in dictation correction, i.e., subjects showed better performance in error correction. In the second experiment, a long concentration test combined with short or continuous noise resulted in negative effects, i.e., subjects made more errors in the subsequent correction of dictation. It can be concluded that school noise can, on the one hand, increase the experience of stress and, on the other hand, promote or limit teachers’ subsequent performance. The latest, however, seems to depend on the specific situation of the individual. In the first part of the third study, the focus was on teachers’ coping styles and experienced stress. Since coping styles have been shown to have a major impact on mental health, it was reasonable to assume that the stress experience caused by noise would vary depending on the coping style. Based on the stress-strain model (Rudow, 2000) and the transactional stress model (Lazarus & Folkman, 1984), it was hypothesized that teachers with risky coping styles experience more stress symptoms. Therefore, an online study was conducted to investigate whether there were differences in psychological and physical symptoms between teachers with different coping styles. For this purpose, 99 Bavarian elementary and middle school teachers were surveyed. Four professional coping styles resulted from the overarching scales of professional commitment and resilience. The healthy type (high commitment, high resilience), the unambitious type (low commitment, high resilience), type A (high commitment, low resilience), and type burnout (low commitment, low resilience) differed in terms of threat appraisal, noise stress, voice and hearing problems, and noise-related burnout. Compared to the healthy type, the risk types − type A and type burnout − exhibited higher stress experience and were generally more susceptible to school noise than the healthy type. This is the first study to show that school noise is particularly hazardous for teachers with risky coping styles. The second part of the third study focused on the impact pathways of school noise. Associations between teachers’ individual characteristics and the consequences of school noise were hypothesized. Based on the simplified model of teacher stress (van Dick & Wagner, 2001), we examined 159 Bavarian elementary and middle school teachers to determine whether noise stress and vocal fatigue mediate the association between noise sensitivity and noise-related burnout. Results indicated that noise stress mediated the relationship between noise sensitivity and vocal fatigue; vocal fatigue mediated the relationship between noise stress and noise-related burnout; noise stress and vocal fatigue serially mediated the relationship between noise sensitivity and noise-related burnout. This is the first study to show links between noise-sensitive teachers and noise stress, voice problems, and noise-related burnout.
Die Zwangsvollstreckung in Kryptowerte am Beispiel des Bitcoins (2021)
Gassner, Daniel
Die Digitalisierung des Geldes durch die Einführung des elektronischen Zahlungsverkehrs in diesem Jahrhundert bildet die Grundlage des heutigen unkörperlichen Geldverkehrs. Das Aufkommen neuer rein digitaler Zahlungsarten wie Kryptowährungen setzen diesen Trend der Entmaterialisierung des Geldverkehrs fort. Insofern ist auch das Recht der Zwangsvollstreckung der Frage ausgesetzt, inwieweit die Vollstreckung in solche Werte zur Befriedigung des Gläubigers möglich ist. Dieser Frage geht die vorliegende Dissertation auf Basis des deutschen Vollstreckungsrecht am Beispiel der Kryptowährung Bitcoins nach.
Multi-objective Network Virtualization and its Applicability to Industrial Networks (2022)
Mandarawi, Waseem
Network virtualization provides high flexibility for deploying communication services in dense and heterogeneous environments. Two main approaches (dimensions) that are usually combined exist: Network Function Virtualization (NFV) technologies for functionality virtualization and Virtual Network Embedding (VNE) algorithms for resource virtualization. These approaches can be applied to different network levels, such as factory and enterprise levels of industrial networks. Several objectives and constraints, that might be conflicting, shall be considered when network virtualization is applied, mainly in complex topologies. This thesis proposes a network virtualization model that considers both virtualization dimensions, two network levels, and different objectives and constraints. The network levels considered are two primary levels in industrial networks. However, this consideration does not restrict the model to a particular environment or certain levels. The considered objectivities/constraints are topology, reliability, security, performance, and resource usage. Based on this model, we first build an overall combined solution for autonomic and composite virtual networking. This solution considers both virtualization dimensions, two network levels, and target objectives. Furthermore, this solution combines three novel virtualization sub-approaches that consider performance, reliability, and performance. However, the sub-approaches apply to different combinations of levels and dimensions, and the reliability approach additionally considers the resource usage objective. After presenting all solutions, we map them to the defined model. Regarding applicability to industrial networks, the combined approach is applied to an enterprise-level Industrial Internet of Things (IIoT) use case inspired by the smart factory concept in Industry 4.0. However, the sub-approaches are applied to more specific use cases. The performance and reliability solutions are integrated with relevant components of the Time Sensitive Networks (TSN) standard as a modern technology for industrial networks. The goal is to enrich the reliability and performance capabilities of TSN with the flexibility of network virtualization. In the combined approach, we compose and embed an environment-aware Extended Virtual Network (EVN) that represents the physical devices, virtual application functions, and required Service Function Chains (SFCs). We use the graph transformation method to transform abstract application requirements (represented by an Application Request (AR)) into an EVN. Both EVN composition and embedding methods consider the Substrate Network (SN) topology and different security, reliability, performance, and resource usage policies. These policies are applied with a certain priority and depend on the properties of communicating entities such as location and type. The EVN is embedded using property-based node mapping, reliability-aware branching, and a greedy chain embedding heuristic. The chain embedding heuristic is evaluated using a random topology that represents the use case. The performance sub-approach is NFV-based and is applied to a specific use case with Time-critical Traffic (TCT) flows. We develop and evaluate a complete framework for virtualizing Time-aware Shaper (TAS) using high-performance NFV. The reliability sub-approach is VNE-based and is applied to a specific factory level use case. We develop minimal and maximal branching heuristics based on a reliability-aware k-shortest path algorithm and compare them using a typical factory topology. We then integrate these algorithms with a Frame Replication and Elimination for Reliability (FRER) simulator to realize reliability policies by the autonomic and efficient configuration of a supporting technology. The security sub-approaches are related to both virtualization dimensions and are applied to generic enterprise-level use cases. However, the applicability of the security aspect to industrial networks is only shown in the combined (EVN) approach and its use case. We research the autonomic security management in Network Function Virtualization Infrastructure (NFVI) with the main goal of early reaction to threats through SFC reconfiguration through Virtual Network Function (VNF) live migration. This goal is approached by supporting the security measurements with a decision making architecture that considers, on the one hand, the threats and events in the environment and, on the other hand, the Service Level Agreement (SLA) between the NFVI provider and user. For this purpose, we classify the VNF-specific attacks and define possible early detectable behavior patterns. Finally, we develop a security-aware VNE heuristic that considers the security requirements of the Virtual Network (VN) and the security capabilities of the SN. This approach is modified in the combined approach to consider deploying virtualized security VNFs.
Understanding Consumers' Digital Data Disclosure Decision-Making: A Focus on Data Sharing Cooperations, Perceived Risks and Low-Cognitive-Effort Processing (2021)
Steudner, Tobias
Due to the advances of digitalization, firms are able to collect more and more personal consumer data and strive to do so. Moreover, many firms nowadays have a data sharing cooperation with other firms, so consumer data is shared with third parties. Accordingly, consumers are confronted regularly with the decision whether to disclose personal data to such a data sharing cooperation (DSC). Despite privacy research has become highly important, peculiarities of such disclosure settings with a DSC between firms have been neglected until now. To address this gap is the first research objective in this thesis. Another underexplored aspect in privacy research is the impact of low-cognitive-effort decision-making. This is because the privacy calculus, the most dominant theory in privacy research, assumes for consumers a purely cognitive effortful and deliberative disclosure decision-making process. Therefore, to expand this perspective and examine the impact of low-cognitive-effort decision-making is the second research objective in this thesis. Additionally, with the third research objective, this thesis strives to unify and increase the understanding of perceived privacy risks and privacy concerns which are the two major antecedents that reduce consumers’ disclosure willingness. To this end, five studies are conducted: i) essay 1 examines and compares consumers’ privacy risk perception in a DSC disclosure setting with disclosure settings that include no DSC, ii) essay 2 examines whether in a DSC disclosure setting consumers rely more strongly on low-cognitive-effort processing for their disclosure decision, iii) essay 3 explores different consumer groups that vary in their perception of how a DSC affects their privacy risks, iv) essay 4 refines the understanding of privacy concerns and privacy risks and examines via meta-analysis the varying effect sizes of privacy concerns and privacy risks on privacy behavior depending on the applied measurement approach, v) essay 5 examines via autobiographical recall the effects of consumers’ feelings and arousal on disclosure willingness. Overall, this thesis shines light on consumers’ personal data disclosure decision-making: essay 1 shows that the perceived risk associated with a disclosure in a DSC setting is not necessarily higher than to an identical firm without DSC. Also, essay 3 indicates that only for the smallest share of consumers a DSC has a negative impact on their disclosure willingness and that one third of consumers do not intensively think about consequences for their privacy risks arising through a DSC. Additionally, essay 2 shows that a stronger reliance on low-cognitive-effort processing is prevalent in DSC disclosure settings. Moreover, essay 5 displays that even unrelated feelings of consumers can impact their disclosure willingness, but the effect direction also depends on consumers’ arousal level. This thesis contributes in three ways to theory: i) it shines light on peculiarities of DSC disclosure settings, ii) it suggests mechanisms and results of low-effort processing, and iii) it enhances the understanding of perceived privacy risks and privacy concerns as well as their resulting effect sizes. Besides theoretical contributions, this thesis offers practical implications as well: it allows firms to adjust the disclosure setting and the communication with their consumers in a way that makes them more successful in data collection. It also shows that firms do not need to be too anxious about a reduced disclosure willingness due to being part of a DSC. However, it also helps consumers themselves by showing in which circumstances they are most vulnerable to disclose personal data. That consumers become conscious of situations in which they are especially vulnerable to disclose data could serve as a countermeasure: this could prevent that consumers disclose too much data and regret it afterwards. Similarly, this thesis serves as a thought-provoking input for regulators as it emphasizes the importance of low-cognitive-effort processing for consumers’ decision-making, thus regulators may be able to consider this in the future. In sum, this thesis expands knowledge on how consumers decide whether to disclose personal data, especially in DSC settings and regarding low-cognitive-effort processing. It offers a more unified understanding for antecedents of disclosure willingness as well as for consumers’ disclosure decision-making processes. This thesis opens up new research avenues and serves as groundwork, in particular for more research on data disclosures in DSC settings.
The diffusion of modern energy technologies in low-income settings - evidence from rural sub-Saharan Africa (2022)
Lenz, Luciane
This collection of three chapters responds to today’s energy challenges. It explores innovative policy aimed to equip the energy poor with access to improved cooking energy and electricity, looking both at the demand and supply side of modern energy technologies. Concretely, it discusses mechanisms to increase uptake of off-grid solar electricity in rural Rwanda based on experimental demand measurements (Chapter 1), it studies how to diffuse improved cooking technologies in rural Senegal via supply-side mechanisms (Chapter 2), and it identifies the need to target cooking technologies in consideration of the broader household context in rural Senegal and beyond (Chapter 3).
From Complex Sentences to a Formal Semantic Representation using Syntactic Text Simplification and Open Information Extraction (2022)
Niklaus, Christina
Sentences that present a complex linguistic structure act as a major stumbling block for Natural Language Processing (NLP) applications whose predictive quality deteriorates with sentence length and complexity. The task of Text Simplification (TS) may remedy this situation. It aims to modify sentences in order to make them easier to process, using a set of rewriting operations, such as reordering, deletion or splitting. These transformations are executed with the objective of converting the input into a simplified output, while preserving its main idea and keeping it grammatically sound. State-of-the-art syntactic TS approaches suffer from two major drawbacks: first, they follow a very conservative approach in that they tend to retain the input rather than transforming it, and second, they ignore the cohesive nature of texts, where context spread across clauses or sentences is needed to infer the true meaning of a statement. To address these problems, we present a discourse-aware TS framework that is able to split and rephrase complex English sentences within the semantic context in which they occur. By generating a fine-grained output with a simple canonical structure that is easy to analyze by downstream applications, we tackle the first issue. For this purpose, we decompose a source sentence into smaller units by using a linguistically grounded transformation stage. The result is a set of selfcontained propositions, with each of them presenting a minimal semantic unit. To address the second concern, we suggest not only to split the input into isolated sentences, but to also incorporate the semantic context in the form of hierarchical structures and semantic relationships between the split propositions. In that way, we generate a semantic hierarchy of minimal propositions that benefits downstream Open Information Extraction (IE) tasks. To function well, the TS approach that we propose requires syntactically well-formed input sentences. It targets generalpurpose texts in English, such as newswire or Wikipedia articles, which commonly contain a high proportion of complex assertions. In a second step, we present a method that allows state-of-the-art Open IE systems to leverage the semantic hierarchy of simplified sentences created by our discourseaware TS approach in constructing a lightweight semantic representation of complex assertions in the form of semantically typed predicate-argument structures. In that way, important contextual information of the extracted relations is preserved that allows for a proper interpretation of the output. Thus, we address the problem of extracting incomplete, uninformative or incoherent relational tuples that is commonly to be observed in existing Open IE approaches. Moreover, assuming that shorter sentences with a more regular structure are easier to process, the extraction of relational tuples is facilitated, leading to a higher coverage and accuracy of the extracted relations when operating on the simplified sentences. Aside from taking advantage of the semantic hierarchy of minimal propositions in existing Open IE Abstract approaches, we also develop an Open IE reference system, Graphene. It implements a relation extraction pattern upon the simplified sentences. The framework we propose is evaluated within our reference TS implementation DisSim. In a comparative analysis, we demonstrate that our approach outperforms the state of the art in structural TS both in an automatic and a manual analysis. It obtains the highest score on three simplification datasets from two different domains with regard to SAMSA (0.67, 0.57, 0.54), a recently proposed metric targeted at automatically measuring the syntactic complexity of sentences which highly correlates with human judgments on structural simplicity and grammaticality. These findings are supported by the ratings from the human evaluation, which indicate that our baseline implementation DisSim returns fine-grained simplified sentences that achieve a high level of syntactic correctness and largely preserve the meaning of the input. Furthermore, a comparative analysis with the annotations contained in the RST Discourse Treebank (RST-DT) reveals that we are able to capture the contextual hierarchy between the split sentences with a precision of approximately 90% and reach an average precision of almost 70% for the classification of the rhetorical relations that hold between them. Finally, an extrinsic evaluation shows that when applying our TS framework as a pre-processing step, the performance of state-ofthe-art Open IE systems can be improved by up to 32% in precision and 30% in recall of the extracted relational tuples. Accordingly, we can conclude that our proposed discourse-aware TS approach succeeds in transforming sentences that present a complex linguistic structure into a sequence of simplified sentences that are to a large extent grammatically correct, represent atomic semantic units and preserve the meaning of the input. Moreover, the evaluation provides sufficient evidence that our framework is able to establish a semantic hierarchy between the split sentences, generating a fine-grained representation of complex assertions in the form of hierarchically ordered and semantically interconnected propositions. Finally, we demonstrate that state-of-the-art Open IE systems benefit from using our TS approach as a pre-processing step by increasing both the accuracy and coverage of the extracted relational tuples for the majority of the Open IE approaches under consideration. In addition, we outline that the semantic hierarchy of simplified sentences can be leveraged to enrich the output of existing Open IE systems with additional meta information, thus transforming the shallow semantic representation of state-of-the-art approaches into a canonical context-preserving representation of relational tuples.
Judas Iskariot – Eine narrative Analyse der Figur im Matthäusevangelium (2022)
Bauer, Judith
Die Studie betrachtet das Matthäusevangelium als zusammenhängende und durchkomponierte Erzählung, um anhand einer narrativen Analyse eine Charakterisierung der Judasfigur vorzunehmen. Dieser Ansatz berücksichtigt, wie sich die Figur in das Gesamtkonzept des Textes einfügt und welches Erzählinteresse mit ihr verfolgt wird. Diese synchrone, zusammenhängende Lesart erhellt: Judas ist im Matthäusevangelium keine rein historische Figur, sondern seine Darstellung hat eine bestimmte Funktion, die mit dem historischen Kontext, der Pragmatik und der theologischen Konzeption des gesamten Textes zu tun hat. Das Matthäusevangelium behandelt das Thema der Schülerschaft und Nachfolge Jesu, wobei die beiden Pole „Mit-Jesus-Sein“ und „Wenigvertrauen“ eine zentrale Rolle spielen. Vor diesem Horizont steht Judas nicht für den „Bösen“, von dem sich die Leserinnen und Leser abgrenzen sollten, sondern er ist Teil der Nachfolgegemeinschaft. Sein Scheitern könnte im Grunde jeden treffen oder anders gesagt: Jeder könnte Judas sein. Zugleich bietet der Text Strategien an, wie mit derartigem Versagen umzugehen ist: Scheitern darf nicht in die Selbstisolation führen, sondern der Erzählzusammenhang verdeutlicht, dass es eine Möglichkeit der Rückkehr zur Gruppe und Vergebung für einen einsichtigen Sünder gibt.
Four essays on statistical modelling of environmental data (2022)
Behm, Svenia
This dissertation deals with geostatistical, time series, and regression analytical approaches for modelling spatio-temporal processes, using air quality data in the applications. The work is structured into four essays the abstracts of which are given in the following. The first essay is titled 'Spatial detrending revisited: Modelling local trend patterns in NO2-concentration in Belgium and Germany'. It is written in co-authorship by Prof. Dr. Harry Haupt and Dr. Angelika Schmid and published in 2018 in Spatial Statistics 28, pp. 331-351 (https://doi.org/10.1016/j.spasta.2018.04.004). Abstract Short-term predictions of air pollution require spatial modelling of trends, heterogeneities, and dependencies. Two-step methods allow real-time computations by separating spatial detrending and spatial extrapolation into two steps. Existing methods discuss trend models for specific environments and require specification search. Given more complex environments, specification search gets complicated by potential nonlinearities and heterogeneities. This research embeds a nonparametric trend modelling approach in real-time two-step methods. Form and complexity of trends are allowed to vary across heterogeneous environments. The proposed method avoids ad hoc specifications and potential generated predictor problems in previous contributions. Examining Belgian and German air quality and land use data, local trend patterns are investigated in a data driven way and are compared to results computed with existing methods and variations thereof. An important aspect of our empirical illustration is the heterogeneity and superior performance of local trend patterns for both research regions. The findings suggest that a nonparametric spatial trend modelling approach is a valuable tool for real-time predictions of pollution variables: it avoids specification search, provides useful exploratory insights and reduces computational costs. The second essay is titled 'Predictability of hourly nitrogen dioxide concentration'. It is written in co-authorship with Prof. Dr. Harry Haupt and published in 2020 in Ecological Modelling 428, 109076 (https://doi.org/10.1016/j.ecolmodel.2020.109076). Abstract Temporal aggregation of air quality time series is typically used to investigate stylized facts of the underlying series such as multiple seasonal cycles. While aggregation reduces complexity, commonly used aggregates can suffer from non-representativeness or non-robustness. For example, definitions of specific events such as extremes are subjective and may be prone to data contaminations. The aim of this paper is to assess the predictability of hourly nitrogen dioxide concentrations and to explore how predictability depends on (i) level of temporal aggregation, (ii) hour of day, and (iii) concentration level. Exploratory tools are applied to identify structural patterns, problems related to commonly used aggregate statistics and suitable statistical modeling philosophies, capable of handling multiple seasonalities and non-stationarities. Hourly times series and subseries of daily measurements for each hour of day are used to investigate the predictability of pollutant levels for each hour of day, with prediction horizons ranging from one hour to one week ahead. Predictability is assessed by time series cross validation of a loss function based on out-of-sample prediction errors. Empirical evidence on hourly nitrogen dioxide measurements suggests that predictability strongly depends on conditions (i)-(iii) for all statistical models: for specific hours of day, models based on daily series outperform models based on hourly series, while in general predictability deteriorates with exposure level. The third essay is titled 'Agglomeration and infrastructure effects in land use regression models for air pollution – Specification, estimation, and interpretations'. It is written in co-authorship with Dr. Markus Fritsch and published in 2021 in Atmospheric Environment 253, 118337 (https://doi.org/10.1016/j.atmosenv.2021.118337). Abstract Established land use regression (LUR) techniques such as linear regression utilize extensive selection of predictors and functional form to fit a model for every data set on a given pollutant. In this paper, an alternative to established LUR modeling is employed, which uses additive regression smoothers. Predictors and functional form are selected in a data-driven way and ambiguities resulting from specification search are mitigated. The approach is illustrated with nitrogen dioxide (NO2) data from German monitoring sites using the spatial predictors longitude, latitude, altitude and structural predictors; the latter include population density, land use classes, and road traffic intensity measures. The statistical performance of LUR modeling via additive regression smoothers is contrasted with LUR modeling based on parametric polynomials. Model evaluation is based on goodness of fit, predictive performance, and a diagnostic test for remaining spatial autocorrelation in the error terms. Additionally, interpretation and counterfactual analysis for LUR modeling based on additive regression smoothers are discussed. Our results have three main implications for modeling air pollutant concentration levels: First, modeling via additive regression smoothers is supported by a specification test and exhibits superior in- and out-of-sample performance compared to modeling based on parametric polynomials. Second, different levels of prediction errors indicate that NO2 concentration levels observed at background and traffic/industrial monitoring sites stem from different processes. Third, accounting for agglomeration and infrastructure effects is important: NO2 concentration levels tend to increase around major cities, surrounding agglomeration areas, and their connecting road traffic network. The fourth essay is titled 'Outlier detection and visualisation in multi-seasonal time series and its application to hourly nitrogen dioxide concentration'. It is written in single authorship and has not been published yet. Abstract Outlier detection in data on air pollutant recordings is conducted to uncover data points that refer to either invalid measurements or valid but unusually high concentration levels. As air pollutant data is typically characterised by multiple seasonalities, the task of outlier detection is associated with the question of how to deal with such non-stationarities. The present work proposes a method that combines time series segmentation, seasonal adjustment, and standardisation of random variables. While the former two are employed to obtain subseries of homoskedastic data, the latter ensures comparability across the subseries. Further, the standardised version of the seasonally adjusted subseries represents a scaled measure for the outlyingness of each data point in the original time series from its mean and therefore forms a suitable basis for outlier detection. In an empirical application to data on hourly NO2 concentration levels recorded at a traffic monitoring site in Cologne, Germany, over the years 2016 to 2019, the common boxplot criterion is used to examine each standardised seasonally adjusted subseries for positive outliers. The results of the analyses are put into their natural temporal order and displayed in a heatmap layout that provides information on when single and sequential outliers occur.
Private Enforcement und Datenschutzrecht (2022)
Halder, Christoph
Das Durchsetzungssystem der Datenschutz-Grundverordnung (DS-GVO) regelt in den Art. 77 ff. DS-GVO nur ansatzweise die Durchsetzung der Rechte der Betroffenen durch Dritte. Die Arbeit hat sich deshalb zum Ziel gesetzt, Rechtsdurchsetzungsmöglichkeiten von Verbänden und Mitbewerbern in Deutschland genauer zu beleuchten. Hierzu wird zunächst der Spielraum untersucht, den die Datenschutz-Grundverordnung Dritten bei der Rechtsdurchsetzung einräumt, also mit anderen Worten, ob deren Rechtsbehelfssystem eine Sperrwirkung für die Mitgliedstaaten zeitigt. Darauf aufbauend wird näher untersucht, ob das Gesetz gegen den unlauteren Wettbewerb (UWG) und das Gesetz über Unterlassungsklagen bei Verbraucherrechts- und anderen Verstößen (UKlaG) innerhalb des ermittelten Spielraums verbleiben.
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