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
This list gets directly filled from ZHAW's project database. Not all projects may show up due to interlinkage aspects.
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. ...
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 ...
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 ...
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 ...
Scansor 2.0 – AI driven monitoring of complex system landscapes
Designing Business Models for the IoT
This project aims at developing a business model simulation software for evaluating IoT business models. The holistic approach leverages advanced simulation methods and will create new revenue opportunities for Swiss manufacturing companies.
A top-down indicator of lean-green alignment in small and medium-sized enterprises
To address the challenge from global warming, the UNFCCC has given rise to several initiatives to channel financial capital into decarbonization efforts. Among investors, demand increasing for investment vehicles that offer both environmental sustainability as well as economic performance benefits. We aim to design ...
Confidential Data Analytics based on Trusted Execution Environments
Currently there is an unmet need for trust and privacy in multi-party data analytics (e.g. in cloud computing). A new solution approach using hardware-based trusted execution environments is called Confidential Computing. The Zurich-based and investor-backed startup decentriq is a provider of confidential analytics ...
Machine learning for NMR spectroscopy
The goal of this project is to make NMR spectroscopy available to a wider range of applications and to non-experts by the automation of data reduction and analysis steps, in particular by combining deep learning methods for the extraction and a Bayesian approach for the integration and refinement of information. ...
Predicitve Waste Management for SBB Train Stations
We develop a system to optimize the waste collection and disposal on SBB's train stations. The new system will use a container fill level sensor network, a novel waste accumulation forecasting algorithm, and state of the art methods for simulation-based tour-planning.
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.