Data warehouses have been accepted in many enterprises to arrange historical data, regularly provide reports, assist decision making, analyse data and mine potentially valuable information. With decades of evolution and the-state-of-art technologies involved, there are many types of data warehouse architectures appearing in academia and industry. When designing, implementing even refining data warehouse architectures, evaluations and differentiations should be considered to meet the requirements. There are a myriad of data warehouse architecture types with various features for different purposes and different sizes of organizations.

 

When designing, building and refining a data warehouse (DWH) system, there are several issues which people need to consider about: what are differences among DWHAs; how to choose one of them; how can we leverage them in our real situations; if we have legacy one, how to enhance and extend it.

 

Qishan is doing his research in this area and intending to solve these issues. Currently, he is focusing on data warehouse architecture modelling, investigation and evaluation. He is also interested in and has done some research on related novel concepts and state-of-the-art technologies for data management in the context of big data. So some novel data analysis architectures are investigated and proposed by him, which combine or leverage traditional and novel architectures.