Data Innovation Community “Personalized Medicine”

Modern medicine as well generates increasingly large data quantities. Reasons for this development are: the higher resolution data from state-of-the-art diagnostic methods (e.g. magnetic resonance imaging (MRI), IT controlled medical technology, comprehensive medical documentation) and the detailed knowledge about the human genome. As a case in point, there is personalized cancer therapy where the increasing use of software aims at taking terabytes of data from clinical, molecular and medication data in diverse formats. In order to significantly improve treatment results, effective treatment options for each individual patient are distilled from these data.

Within the Data Innovation Community “Personalised Medicine”, important data-driven aspects of personalised medicine are to be explored, such as the need-driven care of patients, IT controlled medical technology or even web-based patient care.

The Data Innovation Community “Personalised Medicine” addresses all companies and research institutions interested in conducting joint research with regard to these aspects. This includes industry user companies and clinics but also companies from the automation and IT industries.

DIC leads

Dr. Andreas Schuppert

Bayer AG

Prof. Dr. Morris Riedel

Forschungszentrum Jülich


If you are interested in working with the Data Innovation Community or visit one of its meetings, please contact the DIC leads via e-mail at

Members can access the internal forums at SAP Jam.

Featured Projects

  • bayer-logo

    MedTrend1: Smart Data Prediction of Trends in Medicine

    MedTrend1 is a proof-of-concept study that aims at identifying social trends with medical relevance out of large amounts of Smart Data. For a successful study, it is crucial to combine Smart Data acquisition, on the one hand, with smart analysis on the other hand.

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  • GPU + In-Memory Data Management for Big Data Analytics

    The global rollout of Smart Meters opens a new business paradigm for utilities with data collection/transaction at such a high volume and velocity. In this project, we develop a toolchain based on In-Memory Data Management and Parallel Data Processing in the GPU. Our aim is to use the processing power of the GPU and the high-throughput and low-latency features of In-Memory databases to develop an adequate Big Data analytics platform. Although the project is primarily concerned with the use case of Smart Meter data, the tool chain is also applicable for Big Data analytics in other domains.

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  • sap-logo

    Association Rule Mining for High Dimensional Master Data

    Traditionally, organizations are using rule-based approaches to discover defects in Master Data. The definition of these rules is expensive for organizations and constrained by the availability of resources with the right domain expertise. The aim of this project is to evaluate with recourse to suggested validation rules the applicability of approaches like association rule mining as a way to support Master Data domain experts.

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  • SDI-X: Smart Data Innovation Processes, Tools, and Operational Concepts

    The BMBF-funded project “Smart Data Innovation processes, tools and operational concepts (SDI-X)” explores appropriate tools and best practices. The aim is to not only facilitate extensive data analysis projects between different research and industry partners but also enable their prompt implementation. The results of the projects will be fully integrated into the SIDL and its projects.

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  • Smart Brain Data Analytics

    Smart Brain Data Analyses

    The project is based on a database of around 700 pictures that show consecutive tissue sections of the human brain. Highly complex image registration procedures that use blockface-images as a reference enable the reproduction of the fixed tissue sections as correctly as possible. These high-resolution images can be used to create such high-resolution 3D-models that are part of the current top-level research at the research center in Jülich. For example, volume, structure, and shape of the brain can be visualized like this.

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