| dc.description.abstract | Since its introduction in 1984, R-tree has been proven to be one of the most practical and well-behaved data structures for accommodating dynamic massive sets of geometric objects and conducting a very diverse set of queries on such datasets in real-world applications. This success has led to a variety of versions, each one trying to tune the performance parameters of the original proposal. Among them, the most prominent one is R*-tree, which employs a number of carefully designed heuristics and is widely ccepted as achieving the best performance in most cases. However, in the presence of actively changing datasets, R*-tree still does not avoid performance tuning with forced reinsertion, i.e. a process that performs a kind of local rebuilding. The latter fact has motivated the investigation of the adaptation of a known dynamization technique, based on carefully triggered local rebuildings, for converting static or semi-dynamic, main memory data structures to dynamic ones onto R*-trees. In this paper, we present LR-trees, a new efficient scheme for dynamic manipulation of large datasets, which combines the search performance of the bulk-loaded R-trees with the updated performance of R*-trees. Experimental results provide evidence on the latter statement and illustrate the superiority of the proposed method. | en |
