Distributed real-time operational data analytics
- Main supervisor: Prof. Lars Kai Hansen, email@example.com (contact person)
- Co-supervisor: Prof. Jan Larsen firstname.lastname@example.org
- Mandatory requirements for all PhD positions
- Please contact the main supervisor for additional requirements
- Propose data analytics for Industry 4.0 that exploit the connectivity and data access features of the Fog Nodes.
- Implement a distributed real-time data analytics solution based on Machine-Learning-as-a-Service.
- Demonstrate the data analytics solution on an industrial use case.
- Distributed data representation and modeling, using the services provided by the Fog Nodes, which are close to the machines (robots, actuators, sensors).
- Distributed real-time machine learning algorithms that use the Fog Computing Platform.
- Implementation of the ML algorithms using ML-as-a-Service microservices.
- Evaluation of the approaches, and quantification of value gains obtained from the improved operations.
Planned visits and collaboration:
- VCE (Dr. Peter Wallin): Demonstrate the data analytics apps on the VCE use case.
Please contact the main supervisor for a detailed description.
Please contact the main supervisor for relevant publications about the topic area.