Semantische Suche

This thesis is a follow-up work to a bachelor thesis written at the chair in 2022.

This work focuses on building a framework for the synthesis of data. In the framework, the synthesis process begins with generating a random dependency graph, specifically a directed acyclic graph. Each node in the graph, except the source nodes, has parent nodes and represents a variable. In the next step, each node is populated with predefined random dependencies. A dependency is a model that determines the value of a variable based on its parent variables. From this structure, datasets can be sampled. Users can control the properties of the causal graph through various parameters and choose from multiple types of dependencies, representing different complexity levels.

Additionally, the sampling process allows for interactivity by enabling the exchange of dependencies during the sampling process. Dependencies can be exchanged with fixed values, probability distributions, or time series functions. This flexibility provides a robust tool for improving and comparing the mentioned algorithms under various conditions.

Previously, manual intervention was required to create and update such models. The CIPM approach can be employed to automatically make a calibrated PCM instance available during the development of software. A prototypical implementation of the CIPM approach targets microservice-based web applications implemented in Java. No implementations for other programming languages exist and the process of adapting the CIPM approach to support another programming language has previously not been explored.

We present an approach to adapting CIPM to support Lua-based sensor applications. A prototypical implementation of the adapted approach was evaluated using real-world Lua-based sensor applications from the SICK AppSpace ecosystem. The evaluation demonstrates the feasibility of the adapted approach, but also reveals minor technical issues with the implementation.

Depending on how a system is to be co-simulated, choosing a co-simulation standard can be challenging, as there are many factors and trade-offs to consider. In this thesis, existing co-simulation standards with discrete-event-based co-simulation support will be researched and compared to one another. This comparison will then be used to choose a co-simulation standard for an exemplary case of hardware-software co-simulation, which will be prototypically implemented and evaluated.

experimental condition (dataset, model, tuning, scoring) and are then ranked according to their performance. To draw conclusions about the performance of the encoders for a set of experimental conditions, one can aggregate the rankings into a consensus ranking. (i.e. taking the median rank) The goal of the thesis is to explore the space of consensus rankings and find all possible consensus rankings. However, running an experiment is a very time-consuming task. Therefore we utilize Active Learning, to avoid running unnecessary experiments. In Active Learning, the learner can choose the data it is trained on and achieves greater accuracy with fewer labeled data.