research

Data Analysis and Data Mining

Driven by digitalization, data now plays a central role in a wide variety of areas of life and the economy. It takes on a wide range of forms and characteristics. Data is being collected, processed, and stored in more and more places. Data analysis focuses on extracting valuable information from this data. Data mining focuses on using statistical methods to identify and describe patterns and hidden relationships within the data. At FZI/ITIV, we are conducting research to perform these analyses as agnostically as possible. Our primary target is to ensure the traceability of the analyses and, consequently, a clear presentation of the results.

Machine Learning

Building on data analysis, data mining, and the resulting understanding of the data, machine learning takes things a step further. Here, algorithms learn the patterns in the data as a statistical model and, after a training phase, can generalize these patterns to new data. Machine learning has proven particularly effective in the form of artificial neural networks and is used, for example, for time series forecasting, anomaly detection, and object detection. At FZI/ITIV, we develop and investigate these methods, for example, in the context of automated driving.

Augmentation Using Generative Artificial Neural Networks

In addition to generalizing to unknown data, artificial neural networks are capable of generating new data (the three images above were generated using text prompts with the Stable Diffusion architecture). In addition to purely artistic and creative work, this approach also allows for the supplementation of missing data points in datasets. This expansion of the dataset is referred to as augmentation. At FZI/ITIV, we are conducting research on the use of augmentation with generative artificial neural networks in the context of developing automated driving functions.

Student Work
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