Industrie 4.0

Data analytics for the fourth industrial revolution, such as proactive service and maintenance of production resources or finding anomalies in production processes.


Smart Cities

Exploring data-driven aspects of urban life, such as traffic control, but also waste disposal or disaster control.



Demand-driven fine-tuning of consumption rate models based on smart meter generated data are examples of our energy analytics efforts.



Data-driven aspects of medicine are explored, such as the need-driven care of patients or IT controlled medical technology.

Featured Projects

  • Enhancing Traffic Flow Forecasting with Environmental Models

    The main task of the project is traffic flow forecasting for a region traffic network. In this project, traffic flow forecasting with environmental models is proposed. Nowadays, traffic flow forecasting considers mainly information from one sensor or one specific roadway. However, information from neighbor sensors and other sensors in the traffic subnet could be leveraged in order to improve the state-of-the-art forecasting models.

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  • Optimization of the production processes at John Deere

    The project mainly aims at the reduction of the rework and the avoidance of errors during the production of tractors at the John Deere factory in Mannheim. These two objectives are realized through a data analysis of the error information, the test protocols and their interdependencies. Based on the results of the data analysis, we can make prognoses and rules for the production planning that help the company to take one step further in the process of self-optimization.

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  • All-Time Parts Prediction (ATP) Demo

    ATP predicts the demand for service parts (especially in the automotive industry) for so-called long-time-buy or all-time-buy decisions. This future demand may cover the next 10-20 years and is difficult to estimate, which often leads to buying way too much. Consequently, after many years of sitting in the warehouse, at the end, huge amounts need to be scrapped. This causes high inventory and warehousing costs, which can be significantly reduced by more accurate demand predictions. The IBM ATP Solution has been developed to do exactly that: to predict all-time demand with high accuracy.

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  • Requirement analysis for energetic construction measures based on historical infrastructure data

    Over several years, the KIT-FM (Facility Management) has collected data with immense value for the operational management, but also for the planning and implementation of future infrastructure developments. This data is also of great interest for researchers. On the one hand, we will examine how the existing infrastructure data evaluated by Smart Data methods can help to draw more accurate conclusions about the operational management and the infrastructure planning. On the other hand, we will drive forward the usability of this data for research and innovation projects.

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  • Examination of various Big Data platforms regarding their performance in forensic data analyses

    The LKA Baden-W├╝rttemberg has a data pool of up to 150 TB per case. Performance is a critical factor in this context, which is why it is necessary to research in advance which Big Data platform should be used. Thus, the project aims at building prototypes which are then used to analyze the runtime and performance of various platforms.

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