The overarching goal of MUMMERING is to create a research tool that encompasses the wealth of new 3D imaging mo-dalities that are surging forward for applications in materials engineering, and to create a doctoral programme that trains 15 early stage researchers (ESRs) in this tool. This is urgently needed to prevent that massive amounts of valuable tomogra-phy data ends on a virtual scrapheap. The challenge of handling and analysing terabytes of 3D data is already limiting the level of scientific insight that is extracted from many data sets. With faster acquisition times and multidimensional modali-ties, these challenges will soon scale to the petabyte regime. To meet this challenge, we will create an open access, open source platform that transparently and efficiently handles the complete workflow from data acquisition, over reconstruction and segmentation to physical modelling, including temporal models, i.e. 3D “movies”. We consider it essential to reach this final step without compromising scientific standards if 3D imaging is to become a pervasive research tool in the visions for Industry 4.0. The 15 ESRs will be enrolled in an intensive network-wide doctoral training programme that covers all aspects of 3D imag-ing and will benefit from a varied track of intersectoral secondments that will challenge and broaden their scope and ap-proach to research. The ESRs will exit the MUMMERING network as highly attractive and employable PhDs with a practical and qualified take on industrial research.

The need for levels of availability and scalability beyond those supported by relational databases has led to the emergence of a new generation of purpose-specific databases grouped under the term NoSQL. In general, NoSQL databases are designed with horizontal scalability as a primary concern and deliver increased availability and fault-tolerance at a cost of temporary inconsistency and reduced durability of data. To balance the requirements for data consistency and availability, organisations increasingly migrate towards hybrid data persistence architectures comprising both relational and NoSQL databases. The consensus is that this trend will only become stronger in the future; critical data will continue to be stored in ACID (predominately relational) databases while non-critical data will be progressively migrated to high-availability NoSQL databases. Moreover, as the volume and the value of natural language content constantly grows, built-in support for sophisticated text processing in data persistence architectures is increasingly becoming essential. The aim of TYPHON is to provide a methodology and an integrated technical offering for designing, developing, querying and evolving scalable architectures for persistence, analytics and monitoring of large volumes of hybrid (relational, graph-based, document-based, natural language etc.) data. TYPHON brings together research partners with a long track record of conducting internationally-leading research on software modelling, domain-specific languages, text mining and data migration, and of delivering research results in the form of robust and widely-used open-source software, industrial partners active in the automotive, earth observation, banking, and motorway operation domains, an industrial advisory board of world-class experts in the fields of databases, business intelligence and analytics, and large-scale data management, and a global consortium including more than 400 organisations from all sectors of IT.