Abstract

Since its introduction in 1997, the M-tree became a respected metric access method (MAM), while remaining, together with its descendants, still the only database-friendly MAM, that is, a dynamic structure persistent in paged index. Although there have been many other MAMs developed over the last decade, most of them require either static or expensive indexing. By contrast, the dynamic M-tree construction allows us to index very large databases in subquadratic time, and simultaneously the index can be maintained up-to-date (i.e., supports arbitrary insertions/deletions). In this article we propose two new techniques improving dynamic insertions in M-tree—the forced reinsertion strategies and so-called hybrid-way leaf selection. Both of the techniques preserve logarithmic asymptotic complexity of a single insertion, while they aim to produce more compact M-tree hierarchies (which leads to faster query processing). In particular, the former technique reuses the well-known principle of forced reinsertions, where the new insertion algorithm tries to re-insert the content of an M-tree leaf that is about to split in order to avoid that split. The latter technique constitutes an efficiency-scalable selection of suitable leaf node wherein a new object has to be inserted. In the experiments we show that the proposed techniques bring a clear improvement (speeding up both indexing and query processing) and also provide a tuning tool for indexing vs. querying efficiency trade-off. Moreover, a combination of the new techniques exhibits a synergic effect resulting in the best strategy for dynamic M-tree construction proposed so far.

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