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.
Big Data Query Processing
The goal of this project is to perform a proof-of-concept for Big Data query processing with an international industry partner. In particular, we investigate if a Big Data solution based on Apache Hadoop and Cloudera’s Impala can handle the complex query workload of our industry partner subject to minimal response ...
NoSQL Data Warehouse
In this project we evaluated query processing features of various NoSQL databases such as Cassandra, Redis, MongoDB, etc. We performed a detailed performance analysis on multi-dimensional point and range queries.
SODES: Swiss Open Data Exploration System
In recent years, national and international institutions, governments and NGOs have made large amounts of data publicly available: there exist literally thousands of open data sources, with temperature measurements, stock market prices, population and income statistics etc. However, most open data sets are provided ...
Development of a plattform for information on foundations with intuitive research functionality that supports NPOs, founders, consultants, administrative offices and research institutions in locating funding opportunities. Core of the plattform is a register of all information on foundations that can be augmented by ...
Online concentration maps of air pollutants
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.