Bulk-insertions into R-trees using the small-tree-large-tree approach

Previous work on brdk loading data focussed at building multidimensional index structures jkam scratcl+ while the probIem of bdk insertions into esisting index structures has been largely overlooked. In this paper, we now propme the STLT (Srnall-Tr~Laxge-Tree) technique, which as opposed to the cument technique of inserting data one by cm% bulk inserts entire new datasets into an active R-tree. STLT considers the new dataset as an R-tree itself (small tree), identifies and prepares a suitable location in the original R-tree (large tree) for insertio~ and lastly performs the insert of the small tree into the large tree. Our experiments confirm that for skewed dahsets STLT consistently outperforms existing techniques (by up to 65%) for data insertion, -whilegenerating an index structure with comparable query retrieval performance. Key-wor& Multidimensional Index Structures, Bulk-loading, Bulk-inwtion, Spatial Databases.