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.
Simulation & Optimization needs HPC
The simulation and optimization is predestined for high performance computing (HPC). Many computing operations are necessary and huge amounts of data are being generated. The requirements have also increased in recent years as the models become more complex. In addition, simulation-based optimization needs a large ...
Development of Algorithms for the Analysis of Football Players and Game Situations based on Motion Data
Prediction of Turnover in Gastronomy
How many guests will visit a restaurant and at what time of the day? Which menus will be ordered? Planning is absolutely crucial in gastronomy but not at all easy. It must be ensured that the correct amount of food is purchased and enough staff is present to run the shop. The planning which has been done intuitively ...
Decision Support System for Predictive Maintenance of Laser Cutting Machines
A new decision support system for predictive maintenance of laser cutting machines is developed. The system provides a platform for condition monitoring, fault detection and prediction of the remaining useful life of systems and components as well as data-driven decision support and prescriptive recommendations for ...
Bio-SODA – Enabling Complex, Semantic Queries to Bioinformatics Databases through Intuitive Searching over Data (SNSF NRP 75 "Big Data")
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 ...
Infectiology++ - Germ Tracking
In this project, we develop a system for the detection and analysis of germ transmission chains in the University Hospital Zurich. The system is based on an expert system solution in combination with machine learning (reinforcement learning).
AC/DC: Accurate Customer Identification on Digital Channels
Every user has a unique digital signature with respect to typing, moving the mouse or accessing a web page. For instance, by analyzing the time between specific keystrokes of many users, we can verify whether user A really is user A or whether she/he has assumed the identity of another user. The goal of this ...
FarmAI – Artificial intelligence for Farming Simulator
Dayzzi - Next Generation: Neural Recommendation System
Recommendation systems are ubiquitous on digital platforms. However, many systems rely either on the availability of large amounts of data that allow for data-driven optimization (collaborative filtering), or they are rather simple and lack the possibility of intelligent recommendations. Especially in the fields of ...
Libra: A One-Tool Solution for MLD4 Compliance
Compared with earlier regulations, the 4th European Money Laundering Directive (MLD4) imposes rigorously increased requirements. It compels obliged entities to conduct in depth screenings of customers and their associations. The Libra Project aims at providing a one tool solution for meeting MLD4 compliance. The ...
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.