Markus Hofmeister
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Markus Hofmeister has a total of 33 co-authors. Here are the top co-authors by number of shared publications:
- Sebastian Mosbach
Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
6 shared publications - Markus Kraft
Cambridge Centre for Carbon Reduction in Chemical Technologies Campus for Research Excellence and Technological Enterprise National Research Foundation CREATE Tower 1 Create Way Singapore 138602 Singapore
5 shared publications - Jethro Akroyd
Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
4 shared publications - George Brownbridge
CMCL, No. 9 Journey Campus, Castle Park, Cambridge, CB30AX, UK
3 shared publications Michael Hillman
CMCL Innovations, Cambridge, UK
3 shared publications- Hou Yee Quek
CARES, Cambridge Centre for Advanced Research and Education in Singapore, 1 Create Way, CREATE Tower, #05-05, 138602, Singapore
2 shared publications
Latest Publications
- Open Access
- 25 July 2025
- Published by Elsevier in Sustainable Cities and Society
- Vol. 131
Beyond connected digital twins – Can digital twins really deliver sustainable cities?
- 25 July 2025
- Published by Elsevier in Sustainable Cities and Society
- Vol. 131
Open AccessBeyond connected digital twins – Can digital twins really deliver sustainable cities?
- Open Access
- 31 July 2024
- Published by Elsevier in Sustainable Cities and Society
- Vol. 113
Fostering urban resilience and accessibility in cities: A dynamic knowledge graph approach
- 31 July 2024
- Published by Elsevier in Sustainable Cities and Society
- Vol. 113
Open AccessFostering urban resilience and accessibility in cities: A dynamic knowledge graph approach
- Open Access
- 10 May 2024
- Published by Cambridge University Press (CUP) in Data-Centric Engineering
- Vol. 5, e14
Semantic agent framework for automated flood assessment using dynamic knowledge graphs
This article proposes a framework of linked software agents that continuously interact with an underlying knowledge graph to automatically assess the impacts of potential flooding events. It builds on the idea of connected digital twins based on the World Avatar dynamic knowledge graph to create a semantically rich asset of data, knowledge, and computational capabilities accessible to humans, applications, and artificial intelligence. We develop three new ontologies to describe and link environmental measurements and their respective reporting stations, flood events, and their potential impact on population and built infrastructure as well as the built environment of a city itself. These coupled ontologies are deployed to dynamically instantiate near real-time data from multiple fragmented sources into the World Avatar. Sequences of autonomous agents connected via the derived information framework automatically assess consequences of newly instantiated data, such as newly raised flood warnings, and cascade respective updates through the graph to ensure up-to-date insights into the number of people and building stock value at risk. Although we showcase the strength of this technology in the context of flooding, our findings suggest that this system-of-systems approach is a promising solution to build holistic digital twins for various other contexts and use cases to support truly interoperable and smart cities.
- 10 May 2024
- Published by Cambridge University Press (CUP) in Data-Centric Engineering
- Vol. 5, e14
Open AccessSemantic agent framework for automated flood assessment using dynamic knowledge graphs
This article proposes a framework of linked software agents that continuously interact with an underlying knowledge graph to automatically assess the impacts of potential flooding events. It builds on the idea of connected digital twins based on the World Avatar dynamic knowledge graph to create a semantically rich asset of data, knowledge, and computational capabilities accessible to humans, applications, and artificial intelligence. We develop three new ontologies to describe and link environmental measurements and their respective reporting stations, flood events, and their potential impact on population and built infrastructure as well as the built environment of a city itself. These coupled ontologies are deployed to dynamically instantiate near real-time data from multiple fragmented sources into the World Avatar. Sequences of autonomous agents connected via the derived information framework automatically assess consequences of newly instantiated data, such as newly raised flood warnings, and cascade respective updates through the graph to ensure up-to-date insights into the number of people and building stock value at risk. Although we showcase the strength of this technology in the context of flooding, our findings suggest that this system-of-systems approach is a promising solution to build holistic digital twins for various other contexts and use cases to support truly interoperable and smart cities.
- 8 May 2024
- Published by Elsevier in Journal of Building Engineering
- Vol. 91
Dynamic knowledge graph applications for augmented built environments through “The World Avatar”
- 8 May 2024
- Published by Elsevier in Journal of Building Engineering
- Vol. 91
Dynamic knowledge graph applications for augmented built environments through “The World Avatar”
- Open Access
- 3 May 2024
- Published by Elsevier in SLAS Technology
- Vol. 29 (3) , 100135
The digital lab manager: Automating research support
- 3 May 2024
- Published by Elsevier in SLAS Technology
- Vol. 29 (3) , 100135
Open AccessThe digital lab manager: Automating research support
- Open Access
- 15 December 2023
- Published by Elsevier in Sustainable Cities and Society
- Vol. 101
Cross-domain flood risk assessment for smart cities using dynamic knowledge graphs
- 15 December 2023
- Published by Elsevier in Sustainable Cities and Society
- Vol. 101
Open AccessCross-domain flood risk assessment for smart cities using dynamic knowledge graphs