Research
Research Agenda
Our research agenda covers the following areas and is conducted within the confines of projects executed with industry partners:
- Database and Big Data technology
- Data Mining, Statistics and Predictive Modeling
- Machine Learning and Graph Analytics
- Information Retrieval and Natural Language Processing
- Business Intelligence and Visual Analytics
- Data Warehousing and Decision Support
- Communication and Visualization of Results
- Privacy, Security and Ethics
- Entrepreneurship and Data Product Design
R&D Projects
This list gets directly filled from ZHAW's project database. Not all projects may show up due to interlinkage aspects.
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DataInc – Intelligent Data Integration and Cleaning
Clean, reliable data is crucial to an increasinglydigitized financial industry. We currently observe alack of consistent, high-quality data across assetclasses which requires costly and time-intensivehuman intervention. We propose an AI-drivensolution to address this issue.
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AUTODIDACT – Automated Video Data Annotation to Empower the ICU Cockpit Platform for Clinical Decision Support
Monitoring diverse sensor signals of patients in intensive care can be key to detect potentially fatal emergencies. But in order to perform the monitoring automatically, the monitoring system has to know what is currently happening to the patient: if the patient is for example currently being moved by medical staff, ...
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Interactive Data Visualization Made Easy
Interactive dashboards are a great tool to display research data, both for experts and the general public. Yet, many researchers lack the required skills to create them. We offer a solution by developing a simple framework enabling researchers to create and deploy such dashboards with their data. We will use the ...
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End-to-End Low-Resource Speech Translation for Swiss German Dialects
This project investigates the application of recent findings in Speech Translation to Swiss German. Speech Translation (ST) is the task of translating spoken utterances in one language into written text in a different language. It serves as an essential tool for breaking down language barriers in various ...
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Ecological and economic process optimization in cement production through machine learning
The goal of this Industry 4.0 project is to stabilize and optimize the clinker burning process in a cement plant using machine learning and improved process analytics. This significantly reduces emissions of carbon dioxide, ammonia and nitrogen oxides, improves clinker quality and reduces the consumption of thermal ...
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Good practices for responsible development of AI-based applications in healthcare
This project will identify proven methods, practices and standards that support responsible research and development of AI systems for health. They will be tested in use cases from medical imaging and neurotechnology, publicly released and published as a guideline of recommended best practices. ...
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Simulation-based comparison of an end-to-end and a platform configuration for injection molding line
The project partner received two layout proposals for a new production line and wanted to compare them in terms of their suitability and performance. In the injection molding process, failure of individual injection molding cavities can occur, which leads to systematic or random missing parts. The system's modules ...
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GraphQueryML – Using Machine Learning to Optimize Queries in Graph Databases (SNSF/DFG)
Optimizing the brain of databases with machine learning:Query optimization is one of the hardest problems of database systems research. A query optimizer can be considered as the “brain” of the system that makes sure that queries are executed efficiently. Even after several decades of research, many sub-problems of ...
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DOSSMA – Detection of Suspicious Social Media Activities
The DOSSMA project will investigate suspicious and malicious behaviour on social media platforms. In a first phase, we will compile an extensive survey report on the areas that are currently being researched, including the respective state-of-the-art, existing solutions and initiatives. This report will serve as a ...
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Scansor 2.0 – AI driven monitoring of complex system landscapes
SNF/NRP project: Bio-SODA
One of the major promises of Big Data lies in the simultaneous mining of multiple sources of data. This is particularly important in life sciences, where different and complementary data are scattered across multiple resources. To overcome this issue, the use of RDF/semantic web technology is emerging, but querying these systems often proves to be too complex for most users—thereby hampering wide development and adoption of these technologies.