For software engineers, conceptually understanding the tools they are using in the context of their projects is a daily challenge and a prerequisite for complex tasks. Textual explanations and code examples serve as knowledge resources for understanding software languages and software technologies. This thesis describes research on integrating and interconnecting existing knowledge resources, which can then be used to assist with understanding and comparing software languages and software technologies on a conceptual level. We consider the following broad research questions that we later refine: What knowledge resources can be systematically reused for recovering structured knowledge and how? What vocabulary already exists in literature that is used to express conceptual knowledge? How can we reuse the online encyclopedia Wikipedia? How can we detect and report on instances of technology usage? How can we assure reproducibility as the central quality factor of any construction process for knowledge artifacts? As qualitative research, we describe methodologies to recover knowledge resources by i.) systematically studying literature, ii.) mining Wikipedia, iii.) mining available textual explanations and code examples of technology usage. The theoretical findings are backed by case studies. As research contributions, we have recovered i.) a reference semantics of vocabulary for describing software technology usage with an emphasis on software languages, ii.) an annotated corpus of Wikipedia articles on software languages, iii.) insights into technology usage on GitHub with regard to a catalog of pattern and iv.) megamodels of technology usage that are interconnected with existing textual explanations and code examples.

Connected vehicles will have a tremendous impact on tomorrow’s mobility solutions. Such systems will heavily rely on information delivery in time to ensure the functional reliability, security and safety. However, the host-centric communication model of today’s networks questions efficient data dissemination in a scale, especially in networks characterized by a high degree of mobility. The Information-Centric Networking (ICN) paradigm has evolved as a promising candidate for the next generation of network architectures. Based on a loosely coupled communication model, the in-network processing and caching capabilities of ICNs are promising to solve the challenges set by connected vehicular systems. In such networks, a special class of caching strategies which take action by placing a consumer’s anticipated content actively at the right network nodes in time are promising to reduce the data delivery time. This thesis contributes to the research in active placement strategies in information-centric and computation-centric vehicle networks for providing dynamic access to content and computation results. By analyzing different vehicular applications and their requirements, novel caching strategies are developed in order to reduce the time of content retrieval. The caching strategies are compared and evaluated against the state-of-the-art in both extensive simulations as well as real world deployments. The results are showing performance improvements by increasing the content retrieval (availability of specific data increased up to 35% compared to state-of-the-art caching strategies), and reducing the delivery times (roughly double the number of data retrieval from neighboring nodes). However, storing content actively in connected vehicle networks raises questions regarding security and privacy. In the second part of the thesis, an access control framework for information-centric connected vehicles is presented. Finally, open security issues and research directions in executing computations at the edge of connected vehicle networks are presented.

Initial goal of the current dissertation was the determination of image-based biomarkers sensitive for neurodegenerative processes in the human brain. One such process is the demyelination of neural cells characteristic for Multiple sclerosis (MS) - the most common neurological disease in young adults for which there is no cure yet. Conventional MRI techniques are very effective in localizing areas of brain tissue damage and are thus a reliable tool for the initial MS diagnosis. However, a mismatch between the clinical fndings and the visualized areas of damage is observed, which renders the use of the standard MRI diffcult for the objective disease monitoring and therapy evaluation. To address this problem, a novel algorithm for the fast mapping of myelin water content using standard multiecho gradient echo acquisitions of the human brain is developed in the current work. The method extents a previously published approach for the simultaneous measurement of brain T1, T∗ 2 and total water content. Employing the multiexponential T∗ 2 decay signal of myelinated tissue, myelin water content is measured based on the quantifcation of two water pools (myelin water and rest) with different relaxation times. Whole brain in vivo myelin water content maps are acquired in 10 healthy controls and one subject with MS. The in vivo results obtained are consistent with previous reports. The acquired quantitative data have a high potential in the context of MS. However, the parameters estimated in a multiparametric acquisition are correlated and constitute therefore an ill-posed, nontrivial data analysis problem. Motivated by this specific problem, a new data clustering approach is developed called Nuclear Potential Clustering, NPC. It is suitable for the explorative analysis of arbitrary dimensional and possibly correlated data without a priori assumptions about its structure. The developed algorithm is based on a concept adapted from nuclear physics. To partition the data, the dynamic behavior of electrically even charged nucleons interacting in a d-dimensional feature space is modeled. An adaptive nuclear potential, comprised of a short-range attractive (Strong interaction) and a long-range repulsive term (Coulomb potential), is assigned to each data point. Thus, nucleons that are densely distributed in space fuse to build nuclei (clusters), whereas single point clusters are repelled (noise). The algorithm is optimized and tested in an extensive study with a series of synthetic datasets as well as the Iris data. The results show that it can robustly identify clusters even when complex configurations and noise are present. Finally, to address the initial goal, quantitative MRI data of 42 patients are analyzed employing NPC. A series of experiments with different sets of image-based features show a consistent grouping tendency: younger patients with low disease grade are recognized as cohesive clusters, while those of higher age and impairment are recognized as outliers. This allows for the definition of a reference region in a feature space associated with phenotypic data. Tracking of the individual's positions therein can disclose patients at risk and be employed for therapy evaluation.

Using semantic data from general-purpose programming languages does not provide the unified experience one would want for such an application. Static error checking is lacking, especially with regards to static typing of the data. Based on the previous work of λ-DL, which integrates semantic queries and concepts as types into a typed λ-calculus, this work takes its ideas a step further to meld them into a real-world programming language. This thesis explores how λ-DL's features can be extended and integrated into an existing language, researches an appropriate extension mechanism and produces Semantics4J, a JastAdd-based Java language semantic data extension for type-safe OWL programming, together with examples of its usage.

The provision of electronic participation services (e-participation) is a complex socio-technical undertaking that needs comprehensive design and implementation strategies. E-participation service providers, in the most cases administrations and governments, struggle with changing requirements that demand more transparency, better connectivity and increased collaboration among different actors. At the same time, less staff are available. As a result, recent research assesses only a minority of e-participation services as successful. The challenge is that the e-participation domain lacks comprehensive approaches to design and implement (e-)participation services. Enterprise Architecture (EA) frameworks have evolved in information systems research as an approach to guide the development of complex socio-technical systems. This approach can guide the design and implementation services, if the collection of organisations with the commonly held goal to provide participation services is understood as an E Participation Enterprise (EE). However, research & practice in the e participation domain has not yet exploited EA frameworks. Consequently, the problem scope that motivates this dissertation is the existing gap in research to deploy EA frameworks in e participation design and implementation. The research question that drives this research is: What methodical and technical guides do architecture frameworks provide that can be used to design and implement better and successful e participation? This dissertation presents a literature study showing that existing approaches have not covered yet the challenges of comprehensive e participation design and implementation. Accordingly, the research moves on to investigate established EA frameworks such as the Zachman Framework, TOGAF, the DoDAF, the FEA, the ARIS, and the ArchiMate for their use. While the application of these frameworks in e participation design and implementation is possible, an integrated approach is lacking so far. The synthesis of literature review and practical insights in design and implementation of e participation services from four projects show the challenges of adapting architecture frameworks for this domain. However, the research shows also the potential of a combination of the different approaches. Consequently, the research moves on to develop the E-Participation Architecture Framework (EPART-Framework). Therefore, the dissertation applies design science research including literature review and action research. Two independent settings test an initial EPART-Framework version. The results yield into the EPART-Framework presented in this dissertation. The EPART-Framework comprises of the EPART-Metamodel with six EPART-Viewpoints, which frame the stakeholder concerns: the Participation Scope, the Participant Viewpoint, the Participation Viewpoint, the Data & Information Viewpoint, the E-participation Viewpoint, and Implementation & Governance Viewpoint. The EPART-Method supports the stakeholders to design the EE and implement e participation and stores its output in an architecture description and a solution repository. It consists of five consecutive phases accompanied by requirements management: Initiation, Design, Implementation and Preparation, Participation, and Evaluation. The EPART-Framework fills the gap between the e participation domain and the enterprise architecture framework domain. The evaluation gives reasonable evidence that the framework is a valuable addition in academia and in practice to improve e-participation design and implementation. The same time, it shows opportunities for future research to extend and advance the framework.

This thesis analyzes the online attention towards scientists and their research topics. The studies compare the attention dynamics towards the winners of important scientific prizes with scientists who did not receive a prize. Web signals such as Wikipedia page views, Wikipedia edits, and Google Trends were used as a proxy for online attention. One study focused on the time between the creation of the article about a scientist and their research topics. It was discovered that articles about research topics were created closer to the articles of prize winners than to scientists who did not receive a prize. One possible explanation could be that the research topics are more closely related to the scientist who got an award. This supports that scientists who received the prize introduced the topics to the public. Another study considered the public attention trends towards the related research topics before and after a page of a scientist was created. It was observed that after a page about a scientist was created, research topics of prize winners received more attention than the topics of scientists who did not receive a prize. Furthermore, it was demonstrated that Nobel Prize winners get a lower amount of attention before receiving the prize than the potential nominees from the list of Citation Laureates of Thompson Reuters. Also, their popularity is going down faster after receiving it. It was also shown that it is difficult to predict the prize winners based on the attention dynamics towards them.

One of the main goals of the artificial intelligence community is to create machines able to reason with dynamically changing knowledge. To achieve this goal, a multitude of different problems have to be solved, of which many have been addressed in the various sub-disciplines of artificial intelligence, like automated reasoning and machine learning. The thesis at hand focuses on the automated reasoning aspects of these problems and address two of the problems which have to be overcome to reach the afore-mentioned goal, namely 1. the fact that reasoning in logical knowledge bases is intractable and 2. the fact that applying changes to formalized knowledge can easily introduce inconsistencies, which leads to unwanted results in most scenarios. To ease the intractability of logical reasoning, I suggest to adapt a technique called knowledge compilation, known from propositional logic, to description logic knowledge bases. The basic idea of this technique is to compile the given knowledge base into a normal form which allows to answer queries efficiently. This compilation step is very expensive but has to be performed only once and as soon as the result of this step is used to answer many queries, the expensive compilation step gets worthwhile. In the thesis at hand, I develop a normal form, called linkless normal form, suitable for knowledge compilation for description logic knowledge bases. From a computational point of view, the linkless normal form has very nice properties which are introduced in this thesis. For the second problem, I focus on changes occurring on the instance level of description logic knowledge bases. I introduce three change operators interesting for these knowledge bases, namely deletion and insertion of assertions as well as repair of inconsistent instance bases. These change operators are defined such that in all three cases, the resulting knowledge base is ensured to be consistent and changes performed to the knowledge base are minimal. This allows us to preserve as much of the original knowledge base as possible. Furthermore, I show how these changes can be applied by using a transformation of the knowledge base. For both issues I suggest to adapt techniques successfully used in other logics to get promising methods for description logic knowledge bases.

The identification of experts for a specific technology or framework produces a large benefit for collaborative software projects. Hence it reduces the communication overhead that is required to identify an expert on the fly. Therefore this thesis describes a tool and approach that can be used to identify an expert that has a specific skill-set. It will mainly focus on the skills and expertise of developers that use the Django framework. By adding more rules to our framework that approach could easily be extended for different technologies or frameworks. The paper will close with a case study on an open source project.

One task of executives and project managers in IT companies or departments is to hire suitable developers and to assign them to suitable problems. In this paper, we propose a new technique that directly leverages previous work experience of developers in a systematic manner. Existing evidence for developer expertise based on the version history of existing projects is analyzed. More specifically, we analyze the commits to a repository in terms of affected API usage. On these grounds, we associate APIs with developers and thus we assess API experience of developers. In transitive closure, we also assess programming domain experience.

Software systems are often developed as a set of variants to meet diverse requirements. Two common approaches to this are "clone-and-owning" and software product lines. Both approaches have advantages and disadvantages. In previous work we and collaborators proposed an idea which combines both approaches to manage variants, similarities, and cloning by using a virtual platform and cloning-related operators. In this thesis, we present an approach for aggregating essential metadata to enable a propagate operator, which implements a form of change propagation. For this we have developed a system to annotate code similarities which were extracted throughout the history of a software repository. The annotations express similarity maintenance tasks, which can then either be executed automatically by propagate or have to be performed manually by the user. In this work we outline the automated metadata extraction process and the system for annotating similarities; we explain how the implemented system can be integrated into the workflow of an existing version control system (Git); and, finally, we present a case study using the 101haskell corpus of variants.