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In this thesis, we propose FeLOF, addressing this challenging setting of outlier detection in feature-evolving and high-dimensional data. Our algorithms extends the well-known Local Outlier Factor algorithm to the feature-evolving stream setting. We employ a variation of StreamHash random hashing projections to create a lower-dimensional feature space embedding, thereby mitigating the effects of the curse of dimensionality. To address non-stationary data distributions, we employ a sliding window approach. FeLOF utilizes efficient data structures to speed up search queries and data updates.

Extensive experiments show that our algorithm achieves state-of-the-art outlier detection performance in the static, row stream and feature-evolving stream settings on real-world benchmark data. Additionally, we provide an evaluation of our StreamHash adaptation, demonstrating its ability to cope with sparsely populated high-dimensional data.

In this thesis, we explore another approach for estimating the expected loss of an ML classifier. The aim is to enhance test data with additional expert knowledge. Inspired by recent feature attribution techniques, such as LIME or Saliency Maps, the idea is that experts annotate inputs not only with desired classes, but also with desired feature attributions. We then explore different methods to derive a large conventional test data set based on few such feature attribution annotations. We empirically evaluate the loss estimates of our approach against ground-truth estimates on large existing test data sets, with a focus on the tradeoff between the number of expert annotations and the achieved estimation accuracy.

Portfolio-basierte Methoden zum Lösen von SAT-Instanzen nutzen die komplementäre Stärke von einer Menge von verschiedenen SAT-Algorithmen aus. Hierbei wird aus einer gegebenen Menge von Algorithmen, dem sogenannten Portfolio, mittels eines Vorhersagemodells derjenige Algorithmus ausgewählt, der die bestmögliche Performance für die betrachtete SAT-Instanz verspricht.

In dieser Arbeit interessieren wir uns besonders für erklärbare Portfolios, sprich für Portfolios, für die man nachvollziehen kann, wie die Vorhersage zu ihrem Ergebnis kommt. Gute Erklärbarkeit resultiert einerseits aus einer geringen Größe des Portfolios, andererseits aus einer reduzierten Komplexität des Vorhersagemodells.

Im Vordergrund der Arbeit liegt das effiziente Finden von kleinen Portfolios aus einer größeren Menge von Algorithmen, sowie den Einfluss der Portfoliogröße auf die Performance des Portfolios.

In this thesis we present an evaluation framework for Automated Feature Generation methods, that is integrated into the scikit-learn framework for Python. We integrate nine Automated Feature Generation methods into this framework. We further evaluate the methods on 91 datasets for classification problems. The datasets in our evaluation have up to 58 features and 12,958 observations. As Machine Learning models we investigate five models including state of the art models like XGBoost.

In this thesis, we propose GAK-SVDD. We combine the well-known SVDD algorithm to detect outliers in an unsupervised fashion with Global Alignment Kernels (GAK), bypassing the feature-extraction step. We evaluate GAK-SVDD's performance on 28 standard benchmark data sets and show that it is on par with its closest competitors. Comparing GAK with a DTW-based kernel, GAK improves the median Balanced Accuracy by 4%. Additionally, we extend our method to the active learning setting and examine the combination of GAK and domain-independent attributes.

In this thesis, we provide a novel approach to enable verification of process models with data values, so-called data-value-aware process models. A distinctive of our approach is to support modification of data values while preserving the verification results. We show the functionality of our approach by conducting the verification of a real-world application: the German 4G spectrum auction model.

However, the already existing methods for time series monitoring tend to ignore the background information such as relationships between components or process structure that is available for almost any complex system. Such background information gives a context to the time series data, and can potentially improve the performance of time series monitoring tasks.

In this bachelor thesis, we show how to incorporate structured background information to improve three different time series monitoring tasks. We perform the experiments on the data from the cloud computing center, where we extract background information from system traces. Additionally, we investigate different representations and quality of background information and conclude that its usefulness is independent from a concrete time series monitoring task.

Es werden LSTMs, GRUs, CTRNNs und Elman Netze untersucht. Die Netze werden dabei untersucht sich einen Punkt zu merken und anschließend nach dem Punkt mit einem virtuellen Roboterarm zu greifen.

Bei LSTM, GRU und Elman Netzen wird auch untersucht wie die Netze die Aufgabe lösen, wenn jedes Neuron nur auf den eigenen Speicher zugreifen kann.

Dabei hat sich herausgestellt, dass LSTMs und GRUs deutlich besser bei den Experimenten bewertet werden als CTRNNs und Elman Netze. Außerdem werden die Rechenzeit und der Zusammenhang zwischen der Anzahl der zu trainierenden Parameter und der Ergebnisse der Experimente verglichen.

With this thesis, we propose PeReDeS, an approach that integrates into the development cycle of modern software projects, and explicitly models an automated performance regression detection system that provides feedback quickly and reduces manual effort for setup and load test analysis. PeReDeS is embedded into pipelines for continuous integration, manages the load test execution and lifecycle, processes load test results and makes feedback available to the authoring developer via reports on the coding platform. We further propose a method for detecting deviations in performance on load test results, based on Welch's t-test. The method is adapted to suit the context of performance regression detection, and is integrated into the PeReDeS detection pipeline. We further implemented our approach and evaluated it with an user study and a data-driven study to evaluate the usability and accuracy of our method.

This thesis evaluates the quality of estimated change sequences in the context of model consistency preservation. To derive such sequences, matching elements across the compared models need to be identified and their differences need to be computed. We evaluate a sequence derivation strategy that matches elements based on their unique identifier and one that establishes a similarity metric between elements based on the elements’ features. As an evaluation baseline, different test suites are created. Each test consists of an initial and changed version of both a UML class diagram and consistent Java source code. Using the different strategies, we derive and propagate change sequences based on the state-based difference of the UML view and evaluate the outcome in both domains. The results show that the identity-based matching strategy is able to derive the correct change sequence in almost all (97 %) of the considered cases. For the similarity-based matching strategy we identify two reoccurring error patterns across different test suites. To address these patterns, we provide an extended similarity-based matching strategy that is able to reduce the occurrence frequency of the error patterns while introducing almost no performance overhead.

In dynamical systems, like gas turbines, transition phases occur after a change in the input control signal. The domain knowledge about the steepness of these transitions can potentially help with the modeling of such systems using the data science approaches. There already exist TGDS approaches that use the information about the limits of the values. However it is currently not clear how to incorporate the information about the steepness of the transitions with them.

In this thesis, we develop three different TGDS approaches to include these transition constraints in recurrent neural networks (RNNs) to improve the modeling of input-output behavior of dynamical systems. We evaluate the approaches on synthetic and real time series data by varying data availability and different degrees of steepness. We conclude that the TGDS approaches are especially helpful for flat transitions and provide a guideline on how to use the available transition constraints in real world problems. Finally, we discuss the required degree of domain knowledge and intellectual implementation effort of each approach.

While some approaches use additional information about the current state of the environment, bandit algorithms tend to ignore domain knowledge that can’t be extracted from data. It is not clear how to incorporate domain knowledge into bandit algorithms and how much improvement this yields.

In this masters thesis we propose two ways to augment bandit algorithms with domain knowledge: a push approach, which influences the distribution of arms to deal with non-stationarity as well as a group approach, which propagates feedback between similar arms. We conduct synthetic and real world experiments to examine the usefulness of our approaches. Additionally we evaluate the effect of incomplete and incorrect domain knowledge. We show that the group approach helps to reduce exploration time, especially for small number of iterations and plays, and that the push approach outperforms contextual and non-contextual baselines for large context spaces.

In unserer Arbeit stellen wir einen Ansatz vor, der aus einer gegebenen repräsentativen Benchmark eine kleinere Teilmenge wählt, die als repräsentative Benchmark dienen soll. Wir definieren dabei, dass eine Benchmark repräsentativ ist, wenn der Graph der Laufzeiten ein festgelegtes Ähnlichkeitsmaß gegenüber der ursprünglichen Benchmark überschreitet. Wir haben hierbei einen BeamSearch-Algorithmus erforscht. Am Ende stellen wir allerdings fest, dass eine zufällige Auswahl besser ist und eine zufällige Auswahl von 10 % der Instanzen ausreicht, um eine repräsentative Benchmark zu liefern.

We extend an existing data flow diagram approach with our concept of trust. Our approach of adding knowledge to a software architecture model and providing a way to analyze model instances for access control violations shall enable software architects to increase the quality of models and further verify access control requirements under uncertainty. We evaluate the applicability based on the availability, the accuracy and the scalability regarding the execution time.

For many explainers this proposed reasoning gives us more information about the inner workings of the model or even about the training data. Since data privacy is becoming an important issue the question arises whether explainers can leak private data. It is unclear what private data can be obtained from different kinds of explanation. In this thesis I adapt three privacy attacks in machine learning to the field of XAI: model extraction, membership inference and training data extraction. The different kinds of explainers are sorted into these categories argumentatively and I present specific use cases how an attacker can obtain private data from an explanation. I demonstrate membership inference and training data extraction for two specific explainers in experiments. Thus, privacy can be breached with the help of explainers.

To close the gap between the development and having up-to-date performance models, the Continuous Integration of Performance Models (CIPM) approach has been proposed. It incorporates automatically executed activities into a Continuous Integration pipeline and is realized with Vitruvius combining Java and the PCM. As a consequence, changes from a commit are extracted to incrementally update the models in the VSUM. To estimate the resource demand in the PCM, the CIPM approach adaptively instruments and monitors the source code.

In previous work, parts of the CIPM pipeline were prototypically implemented and partly evaluated with artificial projects. A pipeline combining the incremental model update and the adaptive instrumentation is absent. Therefore, this thesis presents the combined pipeline adapting and extending the existing implementations. The evaluation is performed with the TeaStore and indicates the correct model update and instrumentation. Nevertheless, there is a gap towards the calibration pipeline.

The goal of this Master’s thesis is to develop and evaluate a framework to efficiently and effectively detect “when” and “where” concept drift occurs in high-dimensional data streams. We introduce stream autoencoder windowing (SAW), an approach based on the online training of an autoencoder, while monitoring its reconstruction error via a sliding window of adaptive size. We will evaluate the performance of our method against synthetic data, in which the characteristics of drifts are known. We then show how our method improves the accuracy of existing classifiers for predictive systems compared to benchmarks on real data streams.

Logs are similar to natural languages. Recent deep learning algorithm Transformer Neural Network has shown outstanding performance in Natural Language Processing (NLP) tasks. Based on these, we adapt Transformer Neural Network to detect outliers from applications logs In an unsupervised way. We compared our algorithm against state-of-the-art log outlier detection algorithms on three widely used benchmark datasets. Our algorithm outperformed state-of-the-art log outlier detection algorithms.

Despite the existing work in the healthcare context, making general observations about the impact of domain knowledge is difficult, as different publications use different knowledge types, prediction tasks and model architectures. It further remains unclear if the findings in healthcare are transferable to other use-cases, as well as how much intellectual effort this requires.

With this Thesis we introduce DomainML, a modular framework to evaluate the impact of domain knowledge on different data science tasks. We demonstrate the transferability and flexibility of DomainML by applying the concepts from healthcare to a cloud system monitoring. We then observe how domain knowledge impacts the model’s prediction performance across both domains, and suggest how DomainML could further be used to refine both the given domain knowledge as well as the quality of the underlying dataset.

Design Space Exploration can be a resource intensive process. An architecture candidate may feature certain properties which disqualify it from consideration as an optimal candidate, regardless of its quality metrics. An example for this would be confidentiality violations in data flows introduced by certain components or combinations of components in the architecture. If these properties can be identified early, quality evaluation can be skipped and the candidate discarded, saving resources.

Currently, analyses for identifying such properties are performed disjunct from the design space exploration process. Optimal candidates are determined first, and analyses are then applied to singular architecture candidates. Our approach augments the PerOpteryx design space exploration pipeline with an additional architecture candidate filter stage, which allows existing generic candidate analyses to be integrated into the DSE process. This enables automatic execution of analyses on architecture candidates during DSE, and early discarding of unwanted candidates before quality evaluation takes place.

We use our filter stage to perform data flow confidentiality analyses on architecture candidates, and further provide a set of example analyses that can be used with the filter. We evaluate our approach by running PerOpteryx on case studies with our filter enabled. Our results indicate that the filter stage works as expected, able to analyze architecture candidates and skip quality evaluation for unwanted candidates.

Die in der Arbeit entwickelte Erweiterung des Palladio-Konzeptes ermöglicht es, diese Analyse schon zur Entwurfszeit anhand eines Modells durchzuführen. Mithilfe der Erweiterung kann analysiert werden, wie oft und in welchem Verhältnis ein Fehler aufgetreten ist, welche Fehlervorkommen miteinander korrelieren und wie schwerwiegend die Auswirkungen der aufgetretenen Fehler für den Systemkontext waren. Neben der Analyse der Fehlerpropagation ermöglicht die Erweiterung die Modellierung von Systemen, die auf das Vorkommen eines Fehlers im Sinne einer Rekonfiguration reagieren. Das Konzept wurde anhand eines sicherheitskritischen Systems aus der Domäne der autonomen Fahrzeuge validiert.

In meinem Ansatz wird ein Schema zur Einordnung entwickelt, das sich an aktuelle Literatur anlehnt und drei grundsätzliche Arten von Entscheidungen unterscheidet: Existenzentscheidungen, Eigenschaftenentscheidungen und Umgebungsentscheidungen. Zur Evaluation wurden Open-Source-Softwareprojekte mit natürlichsprachiger Softwarearchitekturdokumentationen betrachtet und iterativ überprüft, wo das aktuelle Schema verbessert werden kann. Zum Schluss wird vorgestellt, welche der Entscheidungsklassen sich im Palladio Component Model abbilden lassen.

In this thesis we will analyze and visualize semantics from massive document directories. The results will be displayed using the arXiv corpus, which contains domain-specific (computer science) papers of the past thirty years. The analysis will illustrate and give insight about past trends of document directories and how their relationships evolve over time.

When working with large data sets, in many situations one has to deals with a large set data from a single class and only few negative examples from other classes. Learning classifiers, which can assign data points to one of the groups, is known as one-class classification (OCC) or outlier detection.

The objective of this thesis is to develop and evaluate an active learning process to train an OCC. The process uses domain knowledge to reasonably adopt a prior distribution. Knowing that prior distribution, query strategies will be evaluated, which consider the certainty, more detailed the uncertainty, of the estimated class membership scorings. The integration of the prior distribution and the estimation of uncertainty, will be modeled using a gaussian process.

Verfahren der künstlichen Intelligenz auf der Grundlage neuronaler Netzwerke können genutzt werden, um viele der besonders aufwändigen Aufgaben schneller oder sogar besser zu lösen als herkömmliche Methoden. In dieser Arbeit wird ein neuartiges neuronales Netzwerk auf seine Fähigkeit hin untersucht, eine Software allein anhand der Pixeldaten ihrer Benutzeroberfläche zu testen. Des Weiteren wird ein Framework entwickelt, welches mithilfe von leistungsfähigen GPUs den Trainingsvorgang deutlich beschleunigen kann.

The recently introduced Monte Carlo Dependency Estimation (MCDE) framework defines the dependency between a set of variables as the expected value of a stochastic process performed on them. In practice, this expected value is approximated with an estimator which iteratively performs a set of Monte Carlo simulations. In this thesis, we propose several alternative estimators to approximate this expected value. They function in a more dynamic way and also leverage information from previous approximation iterations. Using both probability theory and experiments, we show that our new estimators converge much faster than the original one.

Der theoretische Teil der Arbeit behandelt das bislang ungelöste Problem, einen formalen Beweis für die Korrektheit von hW-inference zu finden. Im praktischen Teil schlagen wir heuristische Optimierungen vor, die die Anzahl der zum Lernen benötigten Eingaben verringern. Diese Heuristiken sind potentiell auch für anderen Lern- oder Testverfahren von endlichen Zustandsautomaten relevant. Endliche Zustandsautomaten sind außerdem ein verbreitetes Modell, das zum automatisiertem Testen von Anwendungen mit graphischen Benutzeroberflächen (GUIs) verwendet wird. Wir erötern, dass mit active inference-Algorithmen besonders präzise Modelle existierender GUI-Anwendugnen gelernt werden können. Insbesondere können dabei interne, nicht sichtbare Zustände der Anwendung unterschieden werden. Die Anwendung wird außerdem bereits durch den interkativen inference-Prozess gründlich getestet. Wir evaluieren diesen Ansatz in einer Fallstudie mit hW-inference.

Insbesondere zu Beginn von Softwareprojekten entstehen diese als handgezeichnete Skizzen auf nicht-digitalen Eingabegeräten wie Papier oder Whiteboards. Das Festhalten von Skizzen dieser Art ist folglich auf eine fotografische Lösung beschränkt. Eine digitale Weiterverarbeitung einer auf einem Bild gesicherten Klassendiagrammskizze ist ohne manuelle Rekonstruktion in ein maschinell verarbeitbares Diagramm nicht möglich.

Maschinelle Lernverfahren können durch eine Skizzenerkennung eine automatisierte Transformation in ein digitales Modell gewährleisten. Voraussetzung für diese Verfahren sind annotierte Trainingsdaten. Für UML-Klassendiagramme sind solche bislang nicht veröffentlicht.

Diese Arbeit beschäftigt sich mit der Erstellung eines Datensatzes annotierter UML-Klassendiagrammskizzen für maschinelle Lernverfahren. Hierfür wird eine Datenerhebung, ein Werkzeug für das Annotieren von UML-Klassendiagrammen und eine Konvertierung der Daten in ein Eingabeformat für das maschinelle Lernen präsentiert. Der annotierte Datensatz wird im Anschluss anhand seiner Vielfältigkeit, Detailtiefe und Größe bewertet. Zur weiteren Evaluation wird der Einsatz des Datensatzes an einem maschinellen Lernverfahren validiert. Das Lernverfahren ist nach dem Training der Daten in der Lage, Knoten mit einem F1-Maß von über 99%, Textpositionen mit einem F1-Maß von über 87% und Kanten mit einem F1-Maß von über 71% zu erkennen. Die Evaluation zeigt folglich, dass sich der Datensatz für den Einsatz maschineller Lernverfahren eignet.

This work aims at correctly enforcing role-based access control (RBAC) policies for Solidity smart contract systems on the architectural and source code level. We extend the standard RBAC model by Sandhu, Ferraiolo, and Kuhn to also incorporate insecure information flows and authorization constraints for roles. We create a metamodel to capture the concepts necessary to describe and enforce RBAC policies on the architectural level. The policies are enforced in the source code by translating the model elements to formal specifications. For this purpose, an automatic code generator is implemented. To reason about the implemented smart contracts on the source code level, tools like solc-verify and Slither are employed and extended. Furthermore, we outline the development process resulting from the presented approach. To evaluate our approach and uncover problems and limitations, we employ a case study using the three smart contract software systems Augur, Fizzy and Palinodia. Additionally, we apply a metamodel coverage analysis to reason about the metamodel’s and the generator’s completeness. Furthermore, we provide an argumentation concerning the approach’s correct enforcement. This evaluation shows how a correct enforcement can be achieved under certain assumptions and when information flows are not considered. The presented approach can detect 100% of manually introduced violations during the case study to the underlying RBAC policies. Additionally, the metamodel is expressive enough to describe RBAC policies and contains no unnecessary elements, since approximately 90% of the created metamodel are covered by the implemented generator. We identify and describe limitations like oracles or public variables.

Bezüglich der Kompression von Informationen über Benutzeroberflächen durch die Autoencoder waren die Ergebnisse dagegen vielversprechend, da die Testdaten auch in hoher Auflösung von 900 x 935 Pixeln mit hoher Genauigkeit rekonstruiert werden konnten. Erste Experimente ergaben, dass die Autoencoder darüber hinaus Fähigkeiten entwickeln, Applikationen mit ähnlichem Farbschema oder ähnlicher Designsprache zu kodieren und wiederzugeben. Ein erstes Fazit über die Fähigkeiten zur Generalisierung fällt daher ebenso positiv aus. Die Genauigkeit der Reproduktion sinkt, wenn die Eingabe farblich oder designtechnisch stark von den Trainingsdaten abweicht.

In dieser Arbeit implementieren wir eine bidirektionale Modell-zu-Text-Transformation, welche die als XML-Dateien persistierten Zustände der FeatureIDE-Modelle in geeignete Modellrepräsentationen überführt, um daraus feingranulare Änderungssequenzen abzuleiten. Diese können zur deltabasierten Konsistenzerhaltung verwendet werden. Für die Modellrepräsentation verwenden wir ein bestehendes Metamodell für Variabilität. Zur Ableitung der Änderungssequenzen wird ein existierendes Konsistenzerhaltungsframework eingesetzt. Wir validieren die Korrektheit der Transformation mithilfe von Round-Trip-Tests. Dabei zeigen wir, dass die in dieser Arbeit implementierte Transformation alle geteilten Informationen zwischen FeatureIDE und dem Variabilitäts-Metamodell korrekt transformiert. Zudem können mithilfe der in dieser Arbeit implementierten Transformation und mit dem verwendeten Konsistenzerhatlungsframework zu 94,44% korrekte feingranulare Änderungssequenzen aus den als XML-Datei persistierten Zuständen der FeatureIDE-Modelle abgeleitet werden.

MCDE quantifies dependencies as the average discrepancy between marginal and conditional distributions. In practice, this value is approximated with a dependency estimator. However, the original implementation of this estimator converges rather slowly, which leads to suboptimal results in terms of statistical power. Moreover, MCDE is only able to quantify dependencies among univariate random variables, but not multivariate ones. In this thesis, we make 2 major improvements to MCDE. First, we propose 4 new dependency estimators with faster convergence. We show that MCDE equipped with these new estimators achieves higher statistical power. Second, we generalize MCDE to GMCDE (Generalized Monte Carlo Dependency Estimation) to quantify dependencies among multivariate random variables. We show that GMCDE inherits all the desirable properties of MCDE and demonstrate its superiority against the state-of-the-art dependency measures with experiments.

In this thesis, we investigated the automatic classification of scientific papers in the Software Engineering domain. In doing so, a classification scheme of paper contents with regard to Research Object, Statement, and Evidence was consolidated. We then investigate in a comparative analysis the machine learning algorithms Naïve Bayes, Support Vector Machine, Multi-Layer Perceptron, Logistic Regression, Decision Tree, and BERT applied to the classification task.

There are multiple examples that highlight the importance of dependency estimation, like knowing there exists a correlation between the regular dose of a drug and the health of a patient helps to understand the impact of a newly manufactured drug. Knowing how the case material, brand, and condition of a watch influences the price on an online marketplace can help to buy watches at a good price. Material sciences can also use dependency estimation to predict many properties of a material before it is synthesized in the lab, so fewer experiments are necessary.

Many dependency estimation algorithms require a large amount of data for a good estimation. But data can be expensive, as an example experiments in material sciences, consume material and take time and energy. As we have the challenge of expensive data collection, algorithms need to be data efficient. But there is a trade-off between the amount of data and the quality of the estimation. With a lack of data comes an uncertainty of the estimation. However, the algorithms do not always quantify this uncertainty. As a result, we do not know if we can rely on the estimation or if we need more data for an accurate estimation.

In this bachelor's thesis we compare different state-of-the-art dependency estimation algorithms using a list of criteria addressing these challenges and more. We partly developed the criteria our self as well as took them from relevant publications. The existing publications formulated many of the criteria only qualitative, part of this thesis is to make these criteria measurable quantitative, where possible, and come up with a systematic approach of comparison for the rest.

From 14 selected criteria, we focus on criteria concerning data efficiency and uncertainty estimation, because they are essential for lowering the cost of dependency estimation, but we will also check other criteria relevant for the application of algorithms. As a result, we will rank the algorithms in the different aspects given by the criteria, and thereby identify potential for improvement of the current algorithms.

We do this in two steps, first we check general criteria in a qualitative analysis. For this we check if the algorithm is capable of guided sampling, if it is an anytime algorithm and if it uses incremental computation to enable early stopping, which all leads to more data efficiency.

We also conduct a quantitative analysis on well-established and representative datasets for the dependency estimation algorithms, that performed well in the qualitative analysis. In these experiments we evaluate more criteria: The robustness, which is necessary for error-prone data, the efficiency which saves time in the computation, the convergence which guarantees we get an accurate estimation with enough data, and consistency which ensures we can rely on an estimation.

Battery based load hiding gained a lot of popularity in recent years as an attempt to guarantee a certain degree of privacy for users in smart grids. Our work evaluates a set of the most common privacy measures for BBLH. For this purpose we define logical natural requirements and score how well each privacy measure complies to each requirement. We achieve this by scoring the response for load profile altering (e.g. noise addition) using measures of displacement. We also investigate the stability of privacy measures toward load profile length and number of bins using specific synthetic data experiments. Results show that certain private measures fail badly to one or many requirements and therefore should be avoided.

However, previous research showed that Load Disaggregation underperforms in the industrial setting compared to the household setting. Also, the domain knowledge available about industrial processes remains unused.

The objective of this thesis was to improve load disaggregation algorithms by incorporating domain knowledge in an industrial setting. First, we identified and formalized several domain knowledge types that exist in the industry. Then, we proposed various ways to incorporate them into the Load Disaggregation algorithms, including Theory-Guided Ensembling, Theory-Guided Postprocessing, and Theory-Guided Architecture. Finally, we implemented and evaluated the proposed methods.

Die Beiträge dieser Arbeit sind das Erarbeiten von sechs verschiedenen Ansätzen zur Anpassung des PCM für unterschiedliche Anwendungsgebiete und deren Einstufung anhand von Bewertungskriterien. Für den dabei vielversprechendsten Ansatz wurde ein detailliertes Konzept entwickelt und prototypisch umgesetzt. Dieser Ansatz, ein Modell im PCM mittels einer feingranularen Hardwaresimulation zu parametrisieren, wird in Form des Prototyps bezüglich seiner Umsetzbarkeit, Erweiterbarkeit und Vollständigkeit evaluiert. Die Evaluation der prototypischen Umsetzung erfolgt unter anderem anhand der Kriterien Benutzbarkeit, Genauigkeit und Performance, die in Relation zum PCM betrachtet werden. Der Prototyp ermöglicht die Ausführung einer Hardwaresimulation mit im PCM spezifizierten Parametern, die Extraktion dabei gemessener Leistungsmerkmale und deren direkte Verwendung in einer Simulation des PCM.

Durch das Hinzufügen dieser Themenbeschriftungen konnten auf den Datensätzen eTour und eAnci Verbesserungen der F1-Werte von bis zu +9.4 % für das Bestimmen von Rückverfolgbarkeitsverbindungen erzielt werden. Dabei lagen die Verbesserungen der Präzisionswerte zwischen +1.5 % und +11.5 %.