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.
PiaBreed: Machine Learning for automated ovulation and birth monitoring in horses
The project comprises the tasks of a comprehensive data collection (Piavita/ University of Bern) and the development of a mobile, non-invasive system (Piavita/ZHAW) for veterinarians and breeders. The goal is to collect important vital data and to develop a new algorithm scheme with which - ovulation in mares can be reliably determined without ...
INODE: Intelligent Open Data Exploration
Data growth and availability as well as data democratization have radically changed data exploration in the last 10 years. Many different data sets, generated by users, systems and sensors, are continuously being collected. These data sets contain information about scientific experiments, health, energy, education etc., and they are highly ...
RealScore - Scanning of Real-World Sheet Music for a Digital Music Stand
ScorePad’s sheet music scanning service works for high quality input; to scale up business, it should work as well for smartphone pictures, used sheets etc. Project RealScore enhances the successful predecessor project by making deep learning adapt to unseen data through unsupervised learning.
Fighting bites with bytes: Promoting public health with crowdsourced tick prevention
Ticks are on the rise and transmit several infectious diseases, leading to serious illness or even death. The smartphone App “Zecke–Tick Prevention helps people, to remember the tick bite location and to check it for potential Lyme disease symptoms. In an interdisciplinary approach, ZHAW-scientists want to find out more about spatial tick risks. ...
NQuest - Natural Language Query Exploration System
There is a huge amount of valuable information hidden in a company's database which is not easily accessible to business people. To query these databases, end users need to know the technical query language SQL as well as the database structure. However, typical end users do not have enough SQL skills to formulate complex queries. Even more so, ...
EU MSCA REFRACT: Repeat protein Function, Refinement, Annotation and Classification of Topologies
REFRACT is an international consortium aiming to extend our knowledge on the mechanism of tandem repeat protein (TRP) function and evolution, establishing a common classification and best practices. Starting from available state of the art computational tools and databases, it aims to drive a new level of TRP characterization leveraging the ...
Smartstones - AI for plant breeding
Goal of the project is a feasibility study to evaluate the potential of diverse AI techniques for opimising plant breeding on the basis of morphological characteristics.
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 for the time being can now be ...
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.