Advanced Implementation Techniques for Scientific Data Warehouses

Data warehouses using a multidimensional view of data have become very popular in both business and science in recent years. Data warehouses for scientific purposes such as medicine and bio-chemistry1 pose several great challenges to existing data warehouse technology. Data warehouses usually use pre-aggregated data to ensure fast query response. However, pre-aggregation cannot be used in practice if the dimension structures or the relationships between facts and dimensions are irregular. A technique for overcoming this limitation and some experimental results are presented. Queries over scientific data warehouses often need to reference data that is external to the data warehouse, e.g., data that is too complex to be handled by current data warehouse technology, data that is ”owned” by other organizations, or data that is updated frequently. An example of this are the public genome databases such as Swissprot. This paper presents a federation architecture that allows the integration of multidimensional warehouse data with complex external data.