Data Mining @ Bosch RTC
We research data mining and large-scale machine learning algorithms for high-performance, distributed and parallel computing environments. Our problems deal with sparse, high-dimensional heterogeneous data that have temporal correlations, missing values and asynchronous streams. Topics that we work on include time-series analysis, latent variable models, sparse and missing data problems, and association rule mining. Our models and methods are implemented in a distributed, parallelized architecture and run on our HPC cluster in order to scale up to Big data sets. We also investigate and benchmark techniques and tools for data mining and big data.
Our focus is on uncovering new domain knowledge, modeling systems, and providing data-driven predictive analytics in order to create new business value for Bosch. Our major areas of focus include: