The mission of the Information Mining department, DaimlerChrysler Re- search and Technology, is exploring, exploiting, and enriching data mining and text mining methods to provide complex decision and product support systems. A key requirement for being able to provide a complex system lies in the different core technologies used, such as symbolic machine learning, statistical learning procedures, association learning, neural networks, distributed data mining, text mining, and model selection procedures. On the basis of these core technologies, we extract and analyze information from data collected in vehicles, from business data, financial data, and documents. Awareness of the above-mentioned technologies together with know-how about other topics like optimization and case-based reasoning build only one part of the expertise of our research department. The complementary part consists of knowledge about different application domains such as computational marketing, computational finance, car market modeling, data cleaning, and knowledge from various technical domains. In this paper some topics of the second part are presented.