Data & Knowledge Engineering
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R-trees, since their introduction in 1984, have beenproven to be one of the most well-behaved practical datastructures for accommodating dynamic massive sets of geometricobjects and conducting a diverse set of queries onsuch data-sets in real-world applications. In this paper weintroduce a new technique for merging two R-trees into anew one of very good quality. Our method avoids boththe employment of bulk insertions and the solution of bulk-loading,from scratch, the new tree using the data of theoriginal trees. Additionally, unlike previous approaches,it does not make any assumptions about data-set distributions.Experimental results provide evidence on the runtimeefficiency of our method and illustrate the good query performanceof the produced indices.