DBM-Tree: A Dynamic Metric Access Method Sensitive to Local Density Data

Metric Access Methods (MAM) are employed to accelerate the processing of similarity queries, such as the range and the k-nearest neighbor queries. Current methods improve the query performance minimizing the number of disk accesses, keeping a constant height of the structures stored on disks (height-balanced trees). The Slim-tree and the M-tree are the most efficient dynamic MAM so far. However, the overlapping between their nodes has a very high influence on their performance. This paper presents a new dynamic MAM called the DBM-tree (DensityBased Metric tree), which can minimize the overlap between high-density nodes by relaxing the height-balancing of the structure. Thus, the height of the tree is larger in denser regions, in order to keep a tradeoff between breadth-searching and depth-searching. Moreover, an optimization algorithm called Shrink is also presented, which improves the performance of an already built DBM-tree by reorganizing the elements among their nodes. Experiments performed over both synthetic and real datasets showed that the DBM-tree is, in average, 50% faster than traditional MAM and reduces the number of distance calculations by up to 72% and disk accesses by up to 54%. After performing the Shrink algorithm, the performance improves up to 30% regarding the number of disk accesses for range and k-nearest neighbor queries. In addition, the DBM-tree scales up well, exhibiting sub-linear performance with growing number of elements in the database.