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.



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


Smart Infrastructure

Untersuchung datengetriebener Aspekter städtischen Lebens, bspw. der Verkehrssteuerung, der Müllentsorgung oder der Katastrophenbewältigung, bedarfsgesteuerte Optimierung von Verbrauchsmodellen, basierend auf Daten intelligenter Stromzähler.

Featured Projects

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    Enhancing Traffic Flow Forecasting with Environmental Models

    In this project, a traffic-flow forecasting method using environmental models is proposed. Nowadays, traffic flow prediction mainly takes into account information from individual, specific sensors. However, information from neighboring sensors and other sensors in the traffic subnet could be used to improve modern prognosis models.

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  • SDSC-BW: Smart data analysis for component manufacturing

    Smart data analyzes support the scheduling of component production at Herrenknecht AG. Within a customer order, it is necessary to produce various components. The core components are generated in individual production orders at the corporate headquarters in Schwanau. Component manufacturing includes cost, planning, production and quality data.

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  • sap-logo

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

    The LKA Baden-Württemberg 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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  • ibm-logo

    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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  • SDSC-BW: Smart data analysis to predict the state of industrial process water

    Water plays an important role in many industrial processes. A smooth running of complex process water systems is the requirement for a functioning cooling process. Different sizes and measured values are decisive. These include, among others, the pH value, the redox value or the conductivity of the system water. In order to monitor these values, a wide variety of sensors capture and make data available.

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