Usage of the Bag Distance Filtering with In-Memory Metric Trees
DOI:
https://doi.org/10.5753/jidm.2024.3060Keywords:
Metric space, Metric trees, Similarity searchAbstract
Metric trees are efficient indexing structures for multidimensional objects defined in terms of a metric space. One possible application is for string similarity search, using the edit distance as the metric function. A previous work proposes clustering objects under leaf nodes and using the bag distance as a filtering step before the edit distance is computed. Cost predictions estimate that the filtering compensates in practical scenarios. The work has important implications when data resides on secondary storage, where nodes have a fixed size that aligns with page disks. In this paper, we expand the discussion by using the bag distance filtering step for in-memory metric trees, where the clusters have no size constraints. We adjust existing metric trees to support leaf nodes with arbitrary cluster sizes and incorporate parameters based on size and density to decide when a leaf node should be subdivided. Experiments show that cluster size can have a substantial impact during both index construction and search. We report the gains achieved in terms of processing cost and the number of distance computations when using the most suited values for the cluster size and density parameters.
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References
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