Bulk loading a data warehouse built upon a UB-Tree

We consider the issue of bulk loading large data sets for the UB-Tree, a multidimensional index structure. Especially in data warehousing (DW), data mining and OLAP it is necessary to have efficient bulk loading techniques, because loading occurs not continuously, but only from time to time with usually large data sets. We propose two techniques, one for initial loading, which creates a new UB-Tree, and one for incremental loading, which adds data to an existing UB-Tree. Both techniques try to minimize I/O and CPU cost. Measurements with artificial data and data of a commercial data warehouse demonstrate that our algorithms are efficient and able to handle large data sets. As well as the UB-Tree, they are easily integrated into a RDBMS.