The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
An Evaluation of Generic Bulk Loading Techniques
Proceedings of the 27th International Conference on Very Large Data Bases
M+-tree: a new dynamical multidimensional index for metric spaces
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Dynamic similarity search in multi-metric spaces
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
SISAP '08 Proceedings of the First International Workshop on Similarity Search and Applications (sisap 2008)
Improving the performance of M-tree family by nearest-neighbor graphs
ADBIS'07 Proceedings of the 11th East European conference on Advances in databases and information systems
Nearest neighbours search using the PM-Tree
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Clustered pivot tables for I/O-optimized similarity search
Proceedings of the Fourth International Conference on SImilarity Search and APplications
Cut-Region: a compact building block for hierarchical metric indexing
SISAP'12 Proceedings of the 5th international conference on Similarity Search and Applications
Faster construction of ball-partitioning-based metric access methods
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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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.