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Course of studies
Retrospective Analysis of Training and Its Response in Marathon Finishers Based on Fitness App Data
(2021)
Ensuring data quality is central to the digital transformation in industry. Business processes such as predictive maintenance or condition monitoring can be implemented or improved based on the available data. In order to guarantee high data quality, a single data validation system are usually used to validate the production data for further use. However, using a single system allows an attacker only to perform one successful attack to corrupt the whole system. We present a new approach in which a data validation system using multiple different validators minimizes the probability of success for the attacker. The validators are arranged in clusters based on their properties. For a validation process, a challenge is given that specifies which validators should perform the current validation. Validation results from other validators are dropped. This ensures that even for more than half of the validators being corrupted anomalies can be detected during the validation process.
SIM in Light of Big Data
(2015)
Scenario Planning: How is big data going to influence the future of smart mobility in Germany?
(2016)
Smart mobility is the future of transportation services in Germany. The implementation and management of smart mobility is impossible without using big data. At the present time,the analysis of big data in Germany is not fully implemented due to existing challenges. The purpose of this research project is to forecast the impact of big data on smart mobility in Germany with the use of scenario planning. In order to receive the most actual scenarios, the input factors were designed in accordance with extensive literature research, and then ratios between all specifications of input factors were compared and evaluated. Thus four unique scenarios were selected for further detailed interpretation to suggest possible influences of big data on smart mobility in Germany