Research Is At The Heart Of Innovation

eccenca originated from the world-class research group of DBpedia and still maintains close ties to researchers and research groups world-wide. On this page, you find some of the research projects eccenca is participating or has been driving over the past.

CLEVER

CLEVER proposes a series of innovations in hardware accelerators, design stack, and middleware software that revolutionize the ability of edge computing platforms to operate federated, leveraging sparse resources that are coordinated to create a powerful swarm of them. CLEVER technologies will support the deep edge computing paradigm, moving computing services closer to the end user or the source of the data to reduce power consumption, reduce capacity requirements, and latency for mission-critical applications.
As part of CLEVER, eccenca will use its data management infrastructure to build intelligent data pipelines that will enable research and industry partners to develop and deploy machine learning models at scale by leveraging federated learning. The CLEVER ecosystem consists of heterogeneous edge nodes with different capabilities (computational power, storage, network) and characteristics (mobility, privacy, ownership). Therefore, the ECC will also be involved in all aspects of model development, including assisting in the development of middleware software components and the efficient use of edge nodes and resources for optimized machine learning.
Learning Model Training and Deployment. In addition, eccenca will support partners in building data integration pipelines based on their use cases and keep the intelligent model up to date and exploring different strategies for deploying their models in production at scale.


Project data
January 2023 - December 2025 | Funded under the HORIZON-KDT-JU-2021 framework and Bundesministerium von Bildung und Forschung (BMBF)| Number of stakeholders: 20

CRYPTO4GRAPH-AI

The overall goal of CRYPTO4GRAPH-AI is to develop a data management framework to train Machine Learning models that use Privacy Enhancing Technologies (PETs) to discover Knowledge Graphs for improved decision making. Knowledge Graphs are gaining popularity in enterprises due to their ability to integrate data from heterogeneous sources, rich metadata, and machine-understandable semantic representation of background knowledge in a unified structure. While data owners are often unwilling to share sensitive data, this data can be valuable for analysis in other contexts for different stakeholders or even for multiple data owners interested in sharing their data. This requires privacy-friendly Machine Learning mechanisms that enable insights without exposing sensitive information. This project will research beyond the unexplored commonalities of these cutting-edge technologies: Knowledge Graphs, Machine Learning, and Cryptography.

Project data

November 2021 - October 2024 | Funded by Bundesministerium für Bildung und Forschung, BMBF

 

CoyPu

The CoyPu project (Cognitive Economy Intelligence Platform for the Resilience of Economic Ecosystems) aims to develop configurable dashboards that provide decision-makers in politics and business with reliable, up-to-date decision-making resources and recommendations for managing crises. Within this framework, a platform is being developed for the integration, structuring, networking, analysis and evaluation of heterogeneous data from economic supply networks as well as the industry environment and social context.

The enterprise-ready, tried-and-tested semantic knowledge graph platform eccenca Corporate Memory forms the technological foundation in the project to integrate the different data sources, to infuse context and domain knowledge into the data and thus to enable explainable AI.

Project data

June 2021 - May 2024 | Funded under the third BMWi funding call "Artificial Intelligence as a Driver for Economically Relevant Ecosystems"

ResKriVer

The ResKriVer project (Communication and Information Platform for Resilient Crisis-Relevant Supply Networks) focuses on preventive crisis management of the supply of critical services and products. The 12 participating organizations include the Berlin Fire Department, Charité Universitätsmedizin Berlin, and German broadcaster rbb, among others. The project will implement a digital platform that uses AI applications to collect, create and communicate crisis-relevant information for supply chains, and to forecast the impact of bottlenecks in the supply chains of companies and the public sector.

The enterprise-ready, tried-and-tested semantic knowledge graph platform eccenca Corporate Memory forms the technological foundation in the project to integrate the different data sources, to infuse context and domain knowledge into the data and thus to enable explainable AI.

Project data

June 2021 - May 2024 | Funded under the third BMWi funding call "Artificial Intelligence as a Driver for Economically Relevant Ecosystems"

New mobility concepts and better data networking are both crucial factors for global economic development. To invent innovative and sustainable mobility concepts, new data-based value-added services are required. MobiVoc develops an open vocabulary for future-oriented mobility solutions.

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On-going cross-research activity

BRAINE stands for Big data pRocessing and Artificial Intelligence at the Network Edge. The project’s overall aim is to boost the development of the edge framework. It focusses on energy efficient hardware and AI empowered software, capable of processing Big Data at the edge, but also supporting security, data privacy, and sovereignty.

eccenca supports the BRAINE project in building the data management foundation for reliable AI functions in edge devices and software systems. Its enterprise knowledge graph software Corporate Memory will function as the main authoring tool and editor in order to create, modify, manage and validate edge computing workflow descriptions. It will also enable the project to link and integrate disparate data sources as well as ensure the correct semantics and traceability of data that becomes input for future AI functions.

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April 2020 - March 2023 |  Funded under the EU Horizon 2020 framework | Number of stakeholders: 27

MINDSET’s overall goal is the development of an interdisciplinary approach, from both a professional and technical perspective, for the real-time, automated processing of (streaming) data for information retrieval using machine learning methods. From this data, companies can extract decision-relevant information, integrate it efficiently into business processes and thereby achieve far-reaching competitive advantages in existing market segments or offer new services.

This project specifically addresses intelligent digital life cycle files for wind turbines to demonstrate the potentials of such life cycle reports for an efficient wind turbine operation. To achieve this, different learning methods will be applied to time series of wind turbines and combined with other methods for creating the digital life cycle report. eccenca contributes extensive experience in the design of integrated platforms and a component for the lifting of structured data sources via semi-automatically created lifting rules using a machine learning process. In addition, eccenca contributes its expertise in GDPR management for tagging the annotation component.

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June 2018 - February 2021 | Funded by Sächsische Aufbaubank (SAB) | Grant reference number: 100341559

The EU-funded iDev40 – Integrated Development 4.0 project leads the digital transformation of singular processes towards an integrated digital supply chain based on the “digital twin” concept. Development, planning and manufacturing will benefit from the “digital twin” concept in terms of highly digitized virtual processes along the whole product lifecycle. Within this project, eccenca’s role is to provide software and methodology for semantic data representation, integration and synchronization. Furthermore, eccenca will enhance the SCORVoc vocabulary and utilize these building blocks in a jointly developed use case with Infineon on lead-time based dynamic pricing.

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June 2018 - April 2021

Goal of the LIMBO project is the development of the next-generation Linked Data Space for Mobility Data. This data space provides additional services for data consumers and producers and will connect the complete data domain of the BMVI (Bundesministerium für Verkehr und digitale Infrastruktur). eccenca is the coordinator of this nine-partner-project and provides the LIMBO data lifting portal.

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June 2017 - August 2020

Despite significant advances in both hardware, software technology as well as user interaction design, smart textiles have not taken off yet beyond prototype stage. In order to reduce entry barriers especially for small and medium enterprises, the GeniusTex project will create cooperation and collaboration opportunities to develop smart textile products, services and business models. In this project, eccenca provides its experience in ontology development and semantic data integration. eccenca is responsible for the quality and development of all ontological artefacts as well as its exploitation by integrating sensor, process and product data into an executable knowledge graph which is used inside of the GeniusTex portal.

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April 2018 - March 2020

The Internet of Things (IoT) brings opportunities to create new services and products, reducing costs for societies, and changing how services are sold and consumed. A critical obstacle to further IoT innovation are the vertical silos that shape today’s IoT landscape. These silos impede the creation of cross-industry, cross-platform and cross-organisational services due to their lack of interoperability and openness. The bIoTope project lays the foundation for creating open innovation ecosystems by providing a platform that enables companies to easily create new IoT systems and to rapidly harness available information using advanced Systems-of-Systems (SoS) capabilities for connected smart objects – with minimal investment.

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January 2016 - December 2018

The project aims to develop, establish and successfully market a new generation of semantic, networked data applications based on the Linked Data paradigm. For this purpose, a regional technology platform will be realized that encompasses both competences, methods and concrete technology modules. The alliance thereby directly links to developments in Web 3.0 and in the area of eGovernment to Agenda 2020.

Project data

July 2015 - June 2018