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.
DISCAP – Digital Infrastructure for Sustainable Consumption and Production (UNEP)
The goal of this project is to lay the technological foundations for empowering various stakeholders to make fact-based decisions to achieve sustainable consumption and production. In particular, we will evaluate how various data science, machine learning and artificial intelligence methods can be used to extract ...
PRISM: Predicting Radicalization Events in Social Media User Timelines
The PRISM project focuses on detecting radicalization events in Social Media networks. Overall, we are interested in unveiling the mechanics that lead to the event of extremist ideology being transferred and incorporated into a social media user’s world view. Specifically, the proposed project aims to identify ...
DataInc – Intelligent Data Integration and Cleaning
Clean, reliable data is crucial to an increasingly digitized financial industry. We currently observe a lack of consistent, high-quality data across asset classes which requires costly and time-intensive human intervention. We propose an AI-driven solution to address this issue.
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, ...
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 ...
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.
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.