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This thesis presents four contributions in the domains of schema/ontology alignment and query processing. First, we present a novel alignment approach, denoted as FiLiPo (Finding Linkage Points), to align the schema of RDF knowledge bases with the response schema of RESTful Web APIs. FiLiPo only requires knowledge about a knowledge base (e.g., class names) but no prior knowledge about the
Web APIs’ data structure. It uses fifteen different string similarity metrics to find an alignment between the schema of a knowledge base and that of aWeb API.
Next, a benchmark system named ETARA (Evaluation Toolkit for API and RDF Alignment) is introduced that was created with the goal to simulate RESTful Web APIs and is able to cover all important characteristics of Web APIs, i.e., latency, timeouts, rate limits and, furthermore, provides configurable response structures (e.g., JSON or XML). Additionally, it was designed to support researchers during
the development of alignment systems.
Afterward, the alignments determined by FiLiPo are used to create a hybrid and federated query processor named TunA (Tunable Query Optimizer forWeb APIs and User Preferences), which allows SPARQL queries combining knowledge bases and RESTful Web APIs and is tunable towards user preferences, i.e., coverage, reliability and execution time. The primary goal of TunA is to return a query result that satisfies the user’s preferences in terms of data quality, even when using unreliable data sources by performing a majority vote over multiple sources.
Lastly, we present a federated query processor, denoted as ORAQL (Overlap and Reliability Aware Query Processing Layer), which uses overlap information to reduce the number of selected sources that are available in a federation. The goal is to reduce redundant data and, hence, improve the query execution speed. Therefore, ORAQL uses a profile feature that provides information about the overlap between all data sources of a federation. Furthermore, we extend the quality estimation of TunA to cover Triple Pattern Fragment interfaces to ensure a user-provided reliability goal.
This thesis serves as proof of concept for the tensile strength simulation-based nonwoven material design. Objective is the adjustment of the parameters of an underlying production process with regard to a desired tensile strength behavior (optimization). As an example, we focus on the nonwoven airlay production and consider a thermobonding procedure for the consolidation of the nonwoven fabrics.
To be able to map production parameters to the associated tensile strength behavior, we present a model-simulation framework composed of a model for the nonwoven fiber structure generation and a model for the nonwovens’ mechanical behavior under vertical load. The model for the fiber structure generation replicates the stochastic fiber lay-down of the airlay production and results in a random three-dimensional fiber web. This web is consolidated using a virtual bonding procedure that mimics the thermobonding of the nonwoven material. The topology of the resulting adhered fiber structure can be described by a graph, which serves as basis for the subsequent tensile strength simulation. The model used for this purpose describes the mechanical behavior of the material at fiber network level. Therefore, the considered fiber structure sample is interpreted as truss and the fiber connections are equipped with a nonlinear material law, which allows to describe the elastic phase of the nonwovens’ tensile strength behavior. The existence and uniqueness of a solution to the model as well as its numerical treatment are discussed. Moreover, we present data reduction strategies that enable more efficient simulations by removing fiber structure parts that do not contribute to the tensile strength behavior.
As it becomes evident from the numerical experiments, a single tensile strength simulation for a production-like virtual sample is already computational demanding. Costs accumulate further, since Monte-Carlo simulations are required to account for the randomness in the fiber structure generation. Thus, direct simulations provide an infeasible basis for the nonwoven material design. This motivates the use of a predictive surrogate for optimization. Therefore, we consider regression-based approaches at different levels of information within the simulation framework. It turns out that the coupling of a polynomial model, for the fiber structure feature inference, with a linear one, for the stress-strain curve inference, yields accurate predictions. Once trained, the regression models allow for efficient evaluations and thus represent a suitable surrogate for the nonwoven material design. In this context, we discuss two exemplary problems of interest for the application: First, a tracking-type problem that aims to find the production parameters that result in a desired tensile strength behavior, expressed in terms of stress-strain curves. Second, an in-corridor maximization problem, which aims to identify the production parameters that maximize the probability of ending up in a specified stress-strain corridor.
Price indices play a vital role in economic measurement as they reflect price levels
and measure price fluctuations. Price level measures are used with macroeconomic
indicators to express them in real terms. These measures are also used to index wages,
rents, and pensions. Furthermore, they are used as a reference for monetary policy
conducted by central banks. Therefore, the provision of accurate price indices is one
of the most important goals of National Statistical Institutes (NSIs), and numerous
studies have been devoted to this goal.
This cumulative dissertation also contributes to this goal. It contains four chapters,
each of which represents a separate research. The first two studies are devoted to
the treatment of seasonal products by using different price index methods. The first
research is co-authored with Ken van Loon. The third research is dedicated to finding
the most accurate method to make price predictions for missing products. The fourth
research is focused on the treatment of products by using different price index methods
when products’ quality characteristics are available.
Measuring the economic activity of a country requires high-quality data of businesses. In the case of Germany, this is not only required at national level, but also at federal state level and for different economic sectors. Important sources for high-quality business data are the business register and, among others, also 14 business surveys which are conducted by the Federal Statistical Office of Germany. However, the quality requirements of the Federal Statistical Office are in contrast to the interests of the businesses themselves. For them, answering to a survey's questionnaire is an additional cost factor, also known as response burden. A high response burden should be avoided, since it can have a negative impact on the quality of the businesses' responses to the surveys. Therefore, sample coordination can be used as a method to control the distribution of response burden while securing high-quality data.
When applying already existing business survey coordination systems, developed by different statistical institutes, legal and administrative standards of German official statistics have to be taken into account. These standards consider different sampling fractions, rotation fractions, periodicity, and stratification of the aforementioned 14 business surveys. Therefore, the aim of this doctoral thesis is to check the existing business survey coordination systems for their applicability in the context of German official statistics and, if necessary, to modify them accordingly. These modifications include the introduction of individual burden indicators which aim to take the individual perception of response burden into account.
For this purpose, several synthetic data sets have been created to test the application of the modified versions of the different business survey coordination systems through Monte Carlo simulation studies. These data sets include a large panel data set, reflecting the landscape of businesses in Rhineland-Palatinate and three smaller, synthetic data sets. The latter have been created with the help of the R package BuSuCo which has been developed within the scope of this thesis. The above mentioned simulation studies are evaluated based on different measures for estimation quality as well as for the concentration and distribution of response burden.
Bilevel problems are optimization problems for which parts of the variables
are constrained to be an optimal solution to another nested optimization
problem. This structure renders bilevel problems particularly well-suited for
modeling hierarchical decision-making processes. They are widely applicable
in areas such as energy markets, transportation systems, security planning,
and pricing. However, the hierarchical nature of these problems also makes
them inherently challenging to solve, both in theory and in practice.
In this thesis, we study different nonlinear problem settings for the
nested optimization problem. First, we focus on nonlinear but convex bilevel
problems with purely integer variables. We propose a solution algorithm that
uses a branch-and-cut framework with tailored cutting planes. We prove
correctness and finite termination of the method under suitable assumptions
and put it into context of existing literature. Moreover, we provide an
extensive numerical study to showcase the applicability of our method and
we compare it to the state-of-the-art approach for a less general setting on
suitable instances from the literature. Furthermore, we discuss challenges that
arise when we try to generalize our approach to the mixed-integer setting.
Next, we study mixed-integer bilevel problems for which the nested
problem has a nonconvex and quadratic objective function, linear constraints,
and continuous variables. We state and prove a complexity-theoretical hardness result for this
problem class and develop a lower and upper bounding scheme to solve
these problems. We prove correctness and finite termination of the proposed
method under suitable assumptions and test its applicability in a numerical
study.
Finally, we consider bilevel problems with continuous variables, where
the nested problem has a convex-quadratic objective function and linear
constraints. We reformulate them as single-level optimization problems using
necessary and sufficient optimality conditions for the nested problem. Then,
we explore the family of so-called P-split reformulations for this single-level
problem and test their applicability in a preliminary numerical study.
Entrepreneurship is recognized as an important discipline to achieve sustainable development and to address sustainability goals without losing sight of economic aspects. However, entrepreneurship rates are rather low in many industrialized countries with high income levels. Research clearly shows that there is a gap in the entrepreneurial process between intentions and subsequent actions. This means that not everyone with entrepreneurial ambitions also follows through and implements actions. This gap also exists for aspects of sustainability. As a result, there is a need to better understand the traditional and sustainability-focused entrepreneurial process in order to increase corresponding actions. This dissertation offers such a comprehensive perspective and sheds light on individual and contextual predictors for traditional and sustainability-focused behavior of entrepreneurs and self-employed across four studies.
The first three studies focus on individual predictors. By providing a systematic literature review with 107 articles, Chapter 2 highlights the ambivalent role of religion for the entrepreneurial process. Relying on the theory of planned behavior (TPB) as theoretical basis, religion can have positive effects on entrepreneurial attitudes and behavioral control, but also negative consequences for other aspects of behavioral control and subjective norms due to religious restrictions.
The quantitative empirical study in Chapter 3 similarly relies on the TPB and sheds light on individual perceptual factors influencing the sustainability-related intention-action gap in entrepreneurship. Using data from the 2021 Global Entrepreneurship Monitor (GEM) Adult Population Survey (APS) including 22,008 early-stage entrepreneurs from 44 countries worldwide, the results support our theoretical reasoning that sustainability-focused intentions are positively related to social entrepreneurial actions. In addition, it is demonstrated that positive perceptual moderators such as self-efficacy and knowing other entrepreneurs as role models strengthen this relationship while a negative perception such as fear of failure restricts social actions in early-stage entrepreneurship.
The next quantitative empirical study in Chapter 4 examines the behavioral consequences of well-being at a sample of 6,955 German self-employed during COVID-19. This chapter builds on two complementary behavioral perspectives to predict how reductions in financial and non-financial well-being relate to investments in venture development. In this regard, reductions in financial well-being are positively related to time investments, supporting the performance feedback perspective in terms of higher search efforts under negative performance. In contrast, reductions in non-financial well-being are negatively related to time and monetary investments, yielding support for the broadening-and-build perspective indicating that negative psychological experiences narrow the thought-action repertoire and hinder resource deployment. The insights across these first three studies about individual predictors indicate that many different, subjective beliefs, perceptions and emotional states can influence the entrepreneurial process making entrepreneurship and self-employment highly individualized disciplines.
The last quantitative empirical study provides an explorative view on a large number of contextual predictors for social and ecological considerations in entrepreneurial actions. Combining GEM data from 2021 on country level with further information from the World Bank and the OECD, a machine learning approach is employed on a sample of 84 countries worldwide. The results suggest that governmental and regulatory as well as cultural factors are relevant to predict social and ecological considerations. Moreover, market-related aspects are shown to be relevant predictors, especially socio-economic factors for social considerations and economic factors for ecological considerations. Overall, the four studies in this dissertation highlight the complexity of the entrepreneurial process being determined by many different individual and contextual factors. Due to the multitude of potential predictors, this dissertation can only give an initial overview of a selection of factors with many more aspects and interdependencies still to be examined by future research.
Zirkularität und zirkulare Geschäftsmodelle in der Holzindustrie: eine empirische Untersuchung
(2025)
Der ökologische Zustand der Erde befindet sich infolge von Umweltverschmutzung, Abfallaufkommen und CO₂-bedingtem Klimawandel in einem kritischen Zustand. Mit rund 40 % trägt der Bau- und Gebäudesektor erheblich zu den globalen Treibhausgasemissionen bei. Holz gilt als klimafreundliche Alternative zu Beton und Stahl, bedarf jedoch ebenfalls einer nachhaltigen Nutzung. Die Kreislaufwirtschaft bietet mit der Wiederverwendung ein zukunftsweisendes Konzept: So sind etwa 45% des beim Rückbau von Gebäuden anfallenden Holzes potenziell als Rohstoff nutzbar. Dadurch werden alternative Rohstoffquellen erschlossen und das Abfallaufkommen reduziert.
Trotz dieses Potenzials liegt der Zirkularitätsgrad der Weltwirtschaft derzeit nur bei 7,2 %. Vor diesem Hintergrund untersucht die Dissertation, welche Wettbewerbsstrategien und welche organisationalen Fähigkeiten die Entwicklung zirkulärer Geschäftsmodelle fördern. Der Fokus liegt auf der Holzindustrie der DACH-Region, die historisch durch forstwirtschaftliche Nachhaltigkeit geprägt ist, jedoch bislang überwiegend linearen Strukturen folgt.
Die Arbeit kombiniert theoretische Fundierung, eine vierjährige Literaturrecherche, Experteninterviews sowie im Zentrum eine quantitative Unternehmensbefragung (n = 200). Daraus wurde eine aktivitätsorientierte Skala zur Bewertung der Zirkularität eines Geschäftsmodells entwickelt. Analysiert wurden drei Perspektiven: Fähigkeiten, Strategien und Stakeholder.
Im Kontext der Fähigkeitsperspektive wurde ermittelt, dass die dynamischen Fähigkeiten positive Implikationen auf die Umsetzung von Zirkularität haben. Im Forschungsfeld der Strategieperspektive wurde deutlich, dass die Innovationsführerschaft positive Effekte auf die Umsetzung der Kreislaufwirtschaft besitzt. Zudem weisen sowohl die Innovationsführerschaft als auch die Qualitätsführerschaft einen positiven indirekten Effekt über die dynamischen Fähigkeiten auf die Entwicklung zirkulärer Geschäftsmodelle auf. Im Rahmen der Stakeholderperspektive wurde eruiert, dass der Stakeholder-Druck im Zusammenwirken mit einem grünen Unternehmensimage eine Katalysator-Wirkung besitzt. Der Einfluss der Interessengruppen führt dazu, dass die Unternehmen ein grünes Image in eine substanzielle Umsetzungsphase überführen. Darüber hinaus wurde ersichtlich, dass der Stakeholder-Druck als zentraler Veränderungsfaktor wirkt. Während die direkten Auswirkungen der dynamischen Fähigkeiten durch den Druck zurückgehen, nehmen die indirekten Effekte auf die Erreichung von Zirkularität zu. Abschließend werden Handlungsempfehlungen für Unternehmen sowie wissenschaftliche Implikationen und zukünftige Forschungsmöglichkeiten abgeleitet.
Case-Based Reasoning (CBR) is a symbolic Artificial Intelligence (AI) approach that has been successfully applied across various domains, including medical diagnosis, product configuration, and customer support, to solve problems based on experiential knowledge and analogy. A key aspect of CBR is its problem-solving procedure, where new solutions are created by referencing similar experiences, which makes CBR explainable and effective even with small amounts of data. However, one of the most significant challenges in CBR lies in defining and computing meaningful similarities between new and past problems, which heavily relies on domain-specific knowledge. This knowledge, typically only available through human experts, must be manually acquired, leading to what is commonly known as the knowledge-acquisition bottleneck.
One way to mitigate the knowledge-acquisition bottleneck is through a hybrid approach that combines the symbolic reasoning strengths of CBR with the learning capabilities of Deep Learning (DL), a sub-symbolic AI method. DL, which utilizes deep neural networks, has gained immense popularity due to its ability to automatically learn from raw data to solve complex AI problems such as object detection, question answering, and machine translation. While DL minimizes manual knowledge acquisition by automatically training models from data, it comes with its own limitations, such as requiring large datasets, and being difficult to explain, often functioning as a "black box". By bringing together the symbolic nature of CBR and the data-driven learning abilities of DL, a neuro-symbolic, hybrid AI approach can potentially overcome the limitations of both methods, resulting in systems that are both explainable and capable of learning from data.
The focus of this thesis is on integrating DL into the core task of similarity assessment within CBR, specifically in the domain of process management. Processes are fundamental to numerous industries and sectors, with process management techniques, particularly Business Process Management (BPM), being widely applied to optimize organizational workflows. Process-Oriented Case-Based Reasoning (POCBR) extends traditional CBR to handle procedural data, enabling applications such as adaptive manufacturing, where past processes are analyzed to find alternative solutions when problems arise. However, applying CBR to process management introduces additional complexity, as procedural cases are typically represented as semantically annotated graphs, increasing the knowledge-acquisition effort for both case modeling and similarity assessment.
The key contributions of this thesis are as follows: It presents a method for preparing procedural cases, represented as semantic graphs, to be used as input for neural networks. Handling such complex, structured data represents a significant challenge, particularly given the scarcity of available process data in most organizations. To overcome the issue of data scarcity, the thesis proposes data augmentation techniques to artificially expand the process datasets, enabling more effective training of DL models. Moreover, it explores several deep learning architectures and training setups for learning similarity measures between procedural cases in POCBR applications. This includes the use of experience-based Hyperparameter Optimization (HPO) methods to fine-tune the deep learning models.
Additionally, the thesis addresses the computational challenges posed by graph-based similarity assessments in CBR. The traditional method of determining similarity through subgraph isomorphism checks, which compare nodes and edges across graphs, is computationally expensive. To alleviate this issue, the hybrid approach seeks to use DL models to approximate these similarity calculations more efficiently, thus reducing the computational complexity involved in graph matching.
The experimental evaluations of the corresponding contributions provide consistent results that indicate the benefits of using DL-based similarity measures and case retrieval methods in POCBR applications. The comparison with existing methods, e.g., based on subgraph isomorphism, shows several advantages but also some disadvantages of the compared methods. In summary, the methods and contributions outlined in this work enable more efficient and robust applications of hybrid CBR and DL in process management applications.
The gender wage gap in labor market outcomes has been intensively investigated for decades, yet it remains a relevant and innovative research topic in labor economics. Chapter 2 of this dissertation explores the pressing issue of gender wage disparity in Ethiopia. By applying various empirical methodologies and measures of occupational segregation, this chapter aims to analyze the role of female occupational segregation in explaining the gender wage gap across the pay distribution. The findings reveal a significant difference in monthly wages, with women consistently earning lower wages across the wage distribution.
Importantly, the result indicates a negative association between female occupational segregation and the average earnings of both men and women. Furthermore, the estimation result shows that female occupational segregation partially explains the gender wage gap at the bottom of the wage distribution. I find that the magnitude of the gender wage gap in the private sector is higher than in the public sector.
In Chapter 3, the Ethiopian Demography and Health Survey data are leveraged to explore the causal relationship between female labor force participation and domestic violence. Domestic violence against women is a pervasive public health concern, particularly in Africa, including Ethiopia, where a significant proportion of women endure various forms of domestic violence perpetrated by intimate partners. Economic empowerment of women through increased participation in the labor market can be one of the mechanisms for mitigating the risk of domestic violence.
This study seeks to provide empirical evidence supporting this hypothesis. Using the employment rate of women at the community level as an instrumental variable, the finding suggests that employment significantly reduces the risk of domestic violence against women. More precisely, the result shows that women’s employment status significantly reduces domestic violence by about 15 percentage points. This finding is robust for different dimensions of domestic violence, such as physical, sexual, and emotional violence.
By examining the employment outcomes of immigrants in the labor market, Chapter 4 extends the dissertation's inquiry to the dynamics of immigrant economic integration into the destination country. Drawing on data from the German Socio-Economic Panel, the chapter scrutinizes the employment gap between native-born individuals and two distinct groups of first-generation immigrants: refugees and other migrants. Through rigorous analysis, Chapter 4 aims to identify the factors contributing to disparities in employment outcomes among these groups. In this chapter, I aim to disentangle the heterogeneity characteristic of refugees and other immigrants in the labor market, thereby contributing to a deeper understanding of immigrant labor market integration in Germany.
The results show that refugees and other migrants are less likely to find employment than comparable natives. The refugee-native employment gap is much wider than other migrant-native employment gap. Moreover, the findings vary by gender and migration categories. While other migrant men do not differ from native men in the probability of being employed, refugee women are the most disadvantaged group compared to other migrant women and native women in the probability of being employed. The study suggests that German language proficiency and permanent resident permits partially explain the lower employment probability of refugees in the German labor market.
Chapter 5 (co-authored with Uwe Jirjahn) utilizes the same dataset to explore the immigrant-native trade union membership gap, focusing on the role of integration in the workplace and into society. The integration of immigrants into society and the workplace is vital not only to improve migrant's performance in the labor market but also to actively participate in institutions such as trade unions. In this study, we argue that the incomplete integration of immigrants into the workplace and society implies that immigrants are less likely to be union members than natives. Our findings show that first-generation immigrants are less likely to be trade union members than natives. Notably, the analysis shows that the immigrant-native gap in union membership depends on immigrants’ integration into the workplace and society. The gap is smaller for immigrants working in firms with a works council and having social contacts with Germans. Moreover, the results reveal that the immigrant-native union membership gap is decreasing in the year since arrival in Germany.
Die Masterarbeit untersucht den Zusammenhang zwischen Libertarismus und Rechtsextremismus, wobei der Fokus auf der Entwicklung der libertären Szene in Deutschland liegt. Zunächst wird ein ausführlicher theoretischer Teil präsentiert, in dem gezeigt wird, dass zwischen einer radikal wirtschaftsliberalen und einer rechtsextremen Weltauffassung partiell gemeinsame Elemente bestehen. Insbesondere werden ein spezifischer Antiegalitarismus, eine Naturalisierung gesellschaftlicher Sachverhalte sowie eine gemeinsame Feindbildkonstruktion als verbindende Merkmale identifiziert, die beide Ideologien, die auf Ungleichwertigkeitsvorstellungen basieren, prägen. Im Anschluss folgt eine empirische Analyse des libertären Magazins eigentümlich frei, das eine zentrale Rolle in der deutschsprachigen libertären Bewegung spielt. Der soziologische Neo-Institutionalismus dient als theoretische Perspektive, um den institutionellen Wandel innerhalb der libertären Szene zu erfassen und zu analysieren. Die empirische Untersuchung bestätigt die theoretischen Annahmen und zeigt, dass sich im libertären Diskurs eine zunehmende Annäherung an rechtsextreme Ideologien vollzieht. Fünf Phasen des institutionellen Wandels werden identifiziert, die mit einer verstärkten Vernetzung der libertären Bewegung mit dem rechtsextremen Spektrum und der Veränderung von Diskursen einhergehen. Die Arbeit kommt zu dem Schluss, dass die libertäre Szene um eigentlich frei dem rechtsextremen Spektrum zuzuordnen ist. Die Untersuchung schlägt vor, den Libertarismus im Rahmen dieser Entwicklung als „Paläolibertarismus“ zu bezeichnen, was auf eine ideologische Nähe zur Alt-Right-Bewegung hinweist. Zentrale Merkmale dieser Ideologie sind neben einer radikal wirtschaftsliberalen Ausrichtung auch die Forderung nach einer Privatisierung gesellschaftlicher Institutionen und die Etablierung von sozialen Autoritäten wie Familie und Kirche zum Schutz des Individuums vor staatlicher Einflussnahme.
