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Item Open Access Concepts and methods for the design, configuration and selection of machine learning solutions in manufacturing(2021) Villanueva Zacarias, Alejandro Gabriel; Mitschang, Bernhard (Prof. Dr.-Ing. habil.)The application of Machine Learning (ML) techniques and methods is common practice in manufacturing companies. They assign teams to the development of ML solutions to support individual use cases. This dissertation refers as ML solution to the set of software components and learning algorithms to deliver a predictive capability based on available use case data, their (hyper) paremeters and technical settings. Currently, development teams face four challenges that complicate the development of ML solutions. First, they lack a formal approach to specify ML solutions that can trace the impact of individual solution components on domain-specific requirements. Second, they lack an approach to document the configurations chosen to build an ML solution, therefore ensuring the reproducibility of the performance obtained. Third, they lack an approach to recommend and select ML solutions that is intuitive for non ML experts. Fourth, they lack a comprehensive sequence of steps that ensures both best practices and the consideration of technical and domain-specific aspects during the development process. Overall, the inability to address these challenges leads to longer development times and higher development costs, as well as less suitable ML solutions that are more difficult to understand and to reuse. This dissertation presents concepts to address these challenges. They are Axiomatic Design for Machine Learning (AD4ML), the ML solution profiling framework and AssistML. AD4ML is a concept for the structured and agile specification of ML solutions. AD4ML establishes clear relationships between domain-specific requirements and concrete software components. AD4ML specifications can thus be validated regarding domain expert requirements before implementation. The ML solution profiling framework employs metadata to document important characteristics of data, technical configurations, and parameter values of software components as well as multiple performance metrics. These metadata constitute the foundations for the reproducibility of ML solutions. AssistML recommends ML solutions for new use cases. AssistML searches among documented ML solutions those that better fulfill the performance preferences of the new use case. The selected solutions are then presented to decision-makers in an intuitive way. Each of these concepts was evaluated and implemented. Combined, these concepts offer development teams a technology-agnostic approach to build ML solutions. The use of these concepts brings multiple benefits, i. e., shorter development times, more efficient development projects, and betterinformed decisions about the development and selection of ML solutions.Item Open Access Models for data-efficient reinforcement learning on real-world applications(2021) Dörr, Andreas; Toussaint, Marc (Prof. Dr.)Large-scale deep Reinforcement Learning is strongly contributing to many recently published success stories of Artificial Intelligence. These techniques enabled computer systems to autonomously learn and master challenging problems, such as playing the game of Go or complex strategy games such as Star-Craft on human levels or above. Naturally, the question arises which problems could be addressed with these Reinforcement Learning technologies in industrial applications. So far, machine learning technologies based on (semi-)supervised learning create the most visible impact in industrial applications. For example, image, video or text understanding are primarily dominated by models trained and derived autonomously from large-scale data sets with modern (deep) machine learning methods. Reinforcement Learning, on the opposite side, however, deals with temporal decision-making problems and is much less commonly found in the industrial context. In these problems, current decisions and actions inevitably influence the outcome and success of a process much further down the road. This work strives to address some of the core problems, which prevent the effective use of Reinforcement Learning in industrial settings. Autonomous learning of new skills is always guided by existing priors that allow for generalization from previous experience. In some scenarios, non-existing or uninformative prior knowledge can be mitigated by vast amounts of experience for a particular task at hand. Typical industrial processes are, however, operated in very restricted, tightly calibrated operating points. Exploring the space of possible actions or changes to the process naively on the search for improved performance tends to be costly or even prohibitively dangerous. Therefore, one reoccurring subject throughout this work is the emergence of priors and model structures that allow for efficient use of all available experience data. A promising direction is Model-Based Reinforcement Learning, which is explored in the first part of this work. This part derives an automatic tuning method for one of themostcommonindustrial control architectures, the PID controller. By leveraging all available data about the system’s behavior in learning a system dynamics model, the derived method can efficiently tune these controllers from scratch. Although we can easily incorporate all data into dynamics models, real systems expose additional problems to the dynamics modeling and learning task. Characteristics such as non-Gaussian noise, latent states, feedback control or non-i.i.d. data regularly prevent using off-the-shelf modeling tools. Therefore, the second part of this work is concerned with the derivation of modeling solutions that are particularly suited for the reinforcement learning problem. Despite the predominant focus on model-based reinforcement learning as a promising, data-efficient learning tool, this work’s final part revisits model assumptions in a separate branch of reinforcement learning algorithms. Again, generalization and, therefore, efficient learning in model-based methods is primarily driven by the incorporated model assumptions (e.g., smooth dynamics), which real, discontinuous processes might heavily violate. To this end, a model-free reinforcement learning is presented that carefully reintroduces prior model structure to facilitate efficient learning without the need for strong dynamic model priors. The methods and solutions proposed in this work are grounded in the challenges experienced when operating with real-world hardware systems. With applications on a humanoid upper-body robot or an autonomous model race car, the proposed methods are demonstrated to successfully model and master their complex behavior.Item Open Access Analyzing code corpora to improve the correctness and reliability of programs(2021) Patra, Jibesh; Pradel, Michael (Prof. Dr.)Bugs in software are commonplace, challenging, and expensive to deal with. One widely used direction is to use program analyses and reason about software to detect bugs in them. In recent years, the growth of areas like web application development and data analysis has produced large amounts of publicly available source code corpora, primarily written in dynamically typed languages, such as Python and JavaScript. It is challenging to reason about programs written in such languages because of the presence of dynamic features and the lack of statically declared types. This dissertation argues that, to build software developer tools for detecting and understanding bugs, it is worthwhile to analyze code corpora, which can uncover code idioms, runtime information, and natural language constructs such as comments. The dissertation is divided into three corpus-based approaches that support our argument. In the first part, we present static analyses over code corpora to generate new programs, to perform mutations on existing programs, and to generate data for effective training of neural models. We provide empirical evidence that the static analyses can scale to thousands of files and the trained models are useful in finding bugs in code. The second part of this dissertation presents dynamic analyses over code corpora. Our evaluations show that the analyses are effective in uncovering unexpected behaviors when multiple JavaScript libraries are included together and to generate data for training bug-finding neural models. Finally, we show that a corpus-based analysis can be useful for input reduction, which can help developers to find a smaller subset of an input that still triggers the required behavior. We envision that the current dissertation motivates future endeavors in corpus-based analysis to alleviate some of the challenges faced while ensuring the reliability and correctness of software. One direction is to combine data obtained by static and dynamic analyses over code corpora for training. Another direction is to use meta-learning approaches, where a model is trained using data extracted from the code corpora of one language and used for another language.Item Open Access Uncertainty-aware visualization techniques(2021) Schulz, Christoph; Weiskopf, Daniel (Prof. Dr.)Nearly all information is uncertainty-afflicted. Whether and how we present this uncertainty can have a major impact on how our audience perceives such information. Still, uncertainty is rarely visualized and communicated. One difficulty is that we tend to interpret illustrations as truthful. For example, it is difficult to understand that a drawn point’s presence, absence, and location may not convey its full information. Similarly, it may be challenging to classify a point within a probability distribution. One must learn how to interpret uncertainty-afflicted information. Accordingly, this thesis addresses three research questions: How can we identify and reason about uncertainty? What are approaches to modeling flow of uncertainty through the visualization pipeline? Which methods are suitable for harnessing uncertainty? The first chapter is concerned with sources of uncertainty. Then, approaches to model uncertainty using descriptive statistics and unsupervised learning are discussed. Also, a model for validation and evaluation of visualization methods is proposed. Further, methods for visualizing uncertainty-afflicted networks, trees, point data, sequences, and time series are presented. The focus lies on modeling, propagation, and visualization of uncertainty. As encodings of uncertainty, we propose wave-like splines and sampling-based transparency. As an overarching approach to adapt existing visualization methods for uncertain information, we identify the layout process (the placement of objects). The main difficulty is that these objects are not simple points but distribution functions or convex hulls. We also develop two stippling-based methods for rendering that utilize the ability of the human visual system to cope with uncertainty. Finally, I provide insight into possible directions for future research.Item Open Access B-splines on sparse grids for uncertainty quantification(2021) Rehme, Michael F.; Pflüger, Dirk (Prof. Dr.)Item Open Access Automated generation of tailored load tests for continuous software engineering(2021) Schulz, Henning; Hoorn, André van (Dr.-Ing.)Continuous software engineering (CSE) aims to produce high-quality software through frequent and automated releases of concurrently developed services. By replaying workloads that are representative of the production environment, load testing can identify quality degradation under realistic conditions. The literature proposes several approaches that extract representative workload models from recorded data. However, these approaches contradict CSE's high pace and automation in three aspects: they require manual parameterization, generate resource-intensive system-level load tests, and lack the means to select appropriate periods from the temporally varying production workload to justify time-consuming testing. This dissertation addresses the automated generation of tailored load tests to reduce the time and resources required for CSE-integrated testing. The tailoring needs to consider the services of interest and select the most relevant workload periods based on their context, such as the presence of a special sale when testing a webshop. Also, we intend to support experts and non-experts with a high degree of automation and abstraction. We develop and evaluate description languages, algorithms, and an automated load test generation approach that integrates workload model extraction, clustering, and forecasting. The evaluation comprises laboratory experiments, industrial case studies, an expert survey, and formal proofs. Our results show that representative context-tailored load tests can be generated by learning a workload model incrementally, enriching it with contextual information, and predicting the expected workload using time series forecasting. For further tailoring the load tests to services, we propose extracting call hierarchies from recorded invocation traces. Dedicated models of evolving manual parameterizations automate the generation process and restore the representativeness of the load tests. Furthermore, the integration of our approach with an automated execution framework enables load testing for non-experts. Following open-science practices, we provide supplementary material online. The proposed approach is a suitable solution for the described problem. Future work should refine specific building blocks the approach leverages. These blocks are the clustering and forecasting techniques from existing work, which we have assessed to be limited for predicting sharply fluctuating workloads, such as load spikes.Item Open Access Ensemble dependency parsing across languages : methodological perspectives(2021) Faleńska, Agnieszka; Kuhn, Jonas (Prof. Dr.)Human language is ambiguous. Such ambiguity occurs at the lexical as well as syntactic level. At the lexical level, the same word can represent different concepts and objects. At the syntactic level, one phrase or a sentence can have more than one interpretation. Language ambiguity is one of the biggest challenges of Natural Language Processing (NLP), i.e., the research field that sits at the intersection of machine learning and linguistics, and that deals with automatic processing of language data. This challenge arises when automatic NLP tools need to resolve ambiguities and select one possible interpretation of a text to approach understanding its meaning. This dissertation focuses on one of the essential Natural Language Processing tasks - dependency parsing. The task involves assigning a syntactic structure called a dependency tree to a given sentence. Parsing is usually one of the processing steps that helps downstream NLP tasks by resolving some of the syntactic ambiguities occurring in sentences. Since human language is highly ambiguous, deciding on the best syntactic structure for a given sentence is challenging. As a result, even state-of-the-art dependency parsers are far from being perfect. Ensemble methods allow for postponing the decision about the best interpretation until several single parsing models express their opinions. Such complementary views on the same problem show which parts of the sentence are the most ambiguous and require more attention. Ensemble parsers find a consensus among such single predictions, and as a result, provide robust and more trustworthy results. Ensemble parsing architectures are commonly regarded as solutions only for experts and overlooked in practical applications. Therefore, this dissertation aims to provide a deeper understanding of ensemble dependency parsers and answer practical questions that arise when designing such approaches. We investigate ensemble models from three core methodological perspectives: parsing time, availability of training resources, and the final accuracy of the system. We demonstrate that in applications where the complexity of the architecture is not a bottleneck, an integration of strong and diverse parsers is the most reliable approach. Such integration provides robust results regardless of the language and the domain of application. However, when the final accuracy of the system can be sacrificed, more efficient ensemble architectures become available. The decision on how to design them has to take into consideration the desired parsing time, the available training data, and the involved single predictors. The main goal of this thesis is to investigate ensemble parsers. However, to design an ensemble architecture for a particular application, it is crucial to understand the similarities and differences in the behavior of its components. Therefore, this dissertation makes contributions of two sorts: (1) we provide guidelines on practical applications of ensemble dependency parsers, but also (2) through the ensembles, we develop a deeper understanding of single parsing models. We primarily focus on differences between the traditional parsers and their recent successors, which use deep learning techniques.Item Open Access Sicherheitsanalysen von Fail-Operational-Systemen für einen Nachweis nach ISO 26262(2021) Schmid, Tobias; Wagner, Stefan (Prof. Dr.)Der Übergang vom teil- auf das hochautomatisierte Fahren stellt eine Entlastung des Fahrers dar, da dieser das Verkehrsgeschehen nicht mehr permanent überwachen muss. Ein Fail-Silent-Verhalten ist im Fehlerfall kein Übergang in einen sicheren Zustand, weswegen Fail-Operational-Systeme für die funktionale Sicherheit notwendig sind. Fail-Operational-Fahrzeugführungen erfordern redundante Architekturen und neuartige Sicherheitskonzepte, um die Fehlertoleranz und eine geeignete Fehlerreaktion sicherzustellen. Einzelne Aspekte solcher Systeme wurden in der Literatur bereits diskutiert, allerdings fehlt bisher ein hinreichender Nachweis der funktionalen Sicherheit von Fail-Operational-Fahrzeugsystemen. In dieser Arbeit wird eine hinreichende Argumentation der funktionalen Sicherheit gemäß des Industriestandards ISO 26262 für Fail-Operational-Fahrzeugsysteme vorgestellt. Basierend auf der Argumentation werden notwendige Sicherheitsanalysen inklusive derer Nachweisziele auf Systemebene identifiziert Vorgehen für die jeweiligen Analysen vorgestellt. Daraus ergeben sich zusätzlich die Schnittstellen von System- und Subsystemanalysen. Für die Analyse gemeinsamer Ausfälle und den Nachweis der Unabhängigkeit redundanter Elemente, werden, auf Basis einer Studie zur Identifikation relevanter Anforderungen, existierende Vorgehen adaptiert und erweitert. Damit ergibt sich ein Vorgehen, dass den Randbedingungen der Entwicklung eines Fail-Operational-Systems in der Automobilindustrie gerecht wird. Das Fail-Operational-Verhalten der Umschaltlogik, welche im Fehlerfall eine redundante Fahrzeugführung aktiviert, wird anhand eines Model-Checking-Ansatzes verifiziert. Durch die Qualifizierung des Werkzeugs wird die Konformität zur ISO 26262 sichergestellt. Für die Analyse der Fehlerpropagation und der Fehlertoleranzzeit wird der Ansatz entsprechend um den Softwareverbund erweitert. Implementierungs- und Rechenaufwand zeigen die Anwendbarkeit der Analysen. Darüber hinaus werden Fehlerbaummodelle aus der Luft- und Raumfahrt für den quantitativen Nachweis von Fail-Operational-Systemen adaptiert und mittels Markov-Modellen validiert. Durch eine Sensitivitätsanalyse erfolgt die Identifikation von Optimierungsansätzen zur Minimierung der Ausfallwahrscheinlichkeit.Item Open Access Leadership gap in agile teams: how developers and scrum masters mature(2021) Spiegler, Simone V.; Wagner, Stefan (Prof. Dr.)An increasing number of companies aim to enable their developers to work in an agile manner. One key success factor that supports teams in working in an agile way is fitting leadership. Therefore, companies aim to understand leadership in such self-organising teams. One agile leadership concept describes a Scrum Master who is supposed to empower the team to work in an agile manner. However, the findings on leadership that unfolds in a self-organising team are controversial. By using Grounded Theory this thesis provides theories on how leadership evolves in agile teams while taking maturity and organisational culture and structure into account. The thesis does not only provide more theoretical underpinning to human aspects of the agile manner but also builds groundwork for future quantitative testing of leadership in agile teams.Item Open Access Architekturkonzepte zur Datenverwaltung in Data Lakes(2021) Giebler, Corinna; Mitschang, Bernhard (Prof. Dr.-Ing.)Die zunehmende Digitalisierung in zahlreichen Bereichen und die damit verbundene Vielzahl an heterogenen Daten, die gespeichert, verwaltet und analysiert werden müssen, stellen eine Herausforderung für traditionelle Datenmanagementkonzepte dar. Insbesondere gilt es, den potentiellen Wert der Daten auszunutzen und durch neue Erkenntnisse Kosten zu senken und Effizienz zu erhöhen. Um die Verwaltung und flexible Analyse der generierten Daten zu ermöglichen, wurde das Konzept des Data Lake entwickelt. Daten heterogener Struktur werden hier in ihrer Rohform gespeichert, sodass auch lange nach ihrer Erfassung beliebige Anwendungsfälle darauf realisiert werden können. Soll allerdings ein solcher Data Lake für die praktische Nutzung in z.B. einem Unternehmen umgesetzt werden, zeigen sich zahlreiche Probleme und Lücken auf. Methodische Grundlagen sind unvollständig, vage oder fehlen ganz. So gibt es keine vollständige Data-Lake-Architektur oder einen Leitfaden, um eine solche zu erstellen. Auch fehlt es an einer passenden Datenorganisation, um die Vielzahl an Anwendungsfällen und Nutzergruppen eines unternehmensweiten Data Lake zu unterstützen. In dieser Arbeit werden diese Lücken adressiert. Hierzu werden drei Forschungsziele formuliert: Z1-Identifikation der Eigenschaften eines Data Lake, Z2-Erstellung eines Leitfadens zur Definition einer vollständigen Data-Lake-Architektur und Z3-Erarbeitung einer internen Data-Lake-Organisation. Diese Forschungsziele werden durch insgesamt sieben Forschungsbeiträge abgedeckt. Hierfür wird zunächst in einer vollständigen Literaturrecherche das Konzept des Data Lake identifiziert und definiert. Im zweiten Schritt stellt diese Arbeit das Data Lake Architecture Framework (DLAF) vor, welches die Definition einer vollständigen Data-Lake-Architektur ermöglicht. Abschließend bietet das Zonenreferenzmodell einen systematischen Ansatz zur Datenorganisation in Data Lakes. Die Umsetzbarkeit der erarbeiteten Lösungen wird mithilfe einer prototypischen Implementierung für ein reales Anwendungsszenario gezeigt. Eine abschließende Evaluation bestätigt, dass die entwickelten Lösungen vollständig sind, zahlreiche Vorteile bieten und so die Industrialisierung von Data Lakes unterstützen.
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