To validate the proposed methodology, a set of six business cases (BCs) will be implemented where the enRichMyData toolbox is used in the context of inter or intra-party data spaces to enrich data and develop novel services. The BCs demonstrate the use of features of the tools and supporting infrastructures.
Besides the applicability of enRichMyData in a set of complementary cases, various domains with different emphasis on various enrichment aspects will be covered, demonstrating the wide scope and applicability of the enRichMyData toolbox.

Marketing data Enrichment for smart-bidding optimization

Digital marketing campaign management involves both configuration and daily optimization of many variables related to keywords and ads. The marketing campaign configuration requires to be aligned with the social behaviour and interests strongly impact the final performance (visits) and profit (revenue). They need to be updated with new keywords very frequently as they reflect the user trends. For that purpose, JOT must relate to external data sources to automate the keyword dataset update. Therefore, more impactful and profitable marketing campaign management requires access to new data and the implementation of analytical solutions to enrich the information linked to the keywords and support all the structural configuration and optimization actions for the campaign.

Bosch proposes a business case from the automotive sector in the context of Catena-X1, an automotive alliance for secure and standardized data exchange that aims at enabling continuous data reuse and sharing for all contributors along the automotive value chain. The business case is centred around automated robotic welding where BO provides components and software solutions for welding machines that are used by factory owners, including BO and its renown car producers, for building body-in-white (BIW) of cars and other car related components. The goal of the business case is to create a data enrichment pipeline for the automation of data preparation for AI-based analytics related to automatic welding robots.

Artificial Intelligence -based Welding Analytics

Service Data Enrichment for Smart Maintenance

Philips designs and produces capital intensive medical imaging systems (e.g., Magnetic Resonance Imaging, Computed Tomography) which are used for diagnosis and image guided therapy in hospitals. Over the years the functionality of this equipment has grown dramatically, which makes maintenance of system, design and verification methods harder with every system release. The main objective of the business case is to enrich the un/semi-structured datasets using external data sources. The most critical information related to system failure can be extracted from the semantically enriched data and used in downstream tasks to help engineers perform the system troubleshooting remotely or on-site.

Spend Network has built and maintains the largest Open Contracting Data Standard Data (OCDS) database in the world, with over 180 million lines of data. OCDS is a data format that allows procurement data to be standardised, providing more robust linked data for the $13 trillion USD global public procurement market. The objective of the European Register of Entities from Known Actions (EUREKA) business case is to build the EUREKA tool as an open data list of organisations using Spend Network’s proven experience of releasing procurement data from a free to use, open tool.

European Register of Entities from Known Actions

Innovation Knowledge Graph for understanding Innovation lifecycle

The business case aims to build and maintain an “Innovation Knowledge Graph” (IKG) by joining several independent streaming data sources constituting the main phases of the innovation lifecycle. The goal is to interconnect, model and understand the relevant aspects of the global innovation ecosystem from the inception of the ideas to their potential realization in business, up to the decision-making at the research policy level. In particular, the resulting knowledge graph aims to analytically support the narrative of stages which could be understood as a “journey of an innovation” throughout the innovation lifecycle.

Following the integration of industrial internet, Big Data, AI, and other new generation information technology with the traditional process technology in the mining industry, the digital and intelligent upgrade of mineral processing has become the development trend. Combining the digital twin technology and characteristics of the mineral processing industry, a digital simulation platform will enhance the application effect of equipment improvement, process optimisation and intelligent control in the whole production cycle of a concentration plant. Industrial data enrichment for mineral processing optimisation will help BGRIMM increase its competitiveness in optimal service for mineral processing plants worldwide.

Industrial Data Enrichment for Mineral Processing Optimization

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