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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.

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Smart Cities

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

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Energy

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

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Medicine

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

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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

  • SDSC-BW: Smart prediction of shipping volumes with AI-models

    Predicting shipping volumes with artificial intelligence instead of intuitive prediction was the goal of the smart data experts at SDSC-BW together with the logistics and transport company LGI. Various algorithms were implemented, for daily, weekly and monthly prediction, and evaluated in order to find the best model. The complex models of SDSC-BW could significantly outperform the prediction models in the comparative analysis.

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  • SDSC-BW: Detect polluted tank levels early with smart data analysis

    Smart software solutions can monitor tank levels and protect storage tanks from idling or overfilling. For this, many different sensor data such as level, temperature and pressure (density) must be recorded. Hectronic GmbH is specialized in these intelligent system solutions for parking, filling station and tank content management. SDSC-BW has now carried out a potential analysis based on the sensor data from various filling media provided by Hectronic and developed a method for detecting impurities in storage tanks as early as possible.

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  • SDSC-BW: Presciently increasing the energy efficiency

    Air needs high expenditure of energy for its compression – to improve the energy efficiency of the necessary compressed air systems is a big issue for the company Mader, manufacturer of compressed air systems. With the support of SDSC-BW, the company has started smart data analysis of its data to explore previously undiscovered patterns.

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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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    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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