Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The F-Onion-tree is a robust access method that slices the metric space into disjoint subspaces to provide quick indexing of complex data in the main memory. Hover, the F-Onion-tree only performs element-by-element insertions into its structure, i.e. it does not introduce a technique to build the index considering all elements of the dataset at once. In this article,  fill this gap.  propose the HeightBL algorithm for bulk-loading F-Onion-trees. Performance tests with real-world data with different volumes and dimensionalities shod that the index produced by the HeightBL algorithm is very compact. Compared with the element-by-element insertion, the size of the index reduced from 53.42% to 71.25%. The experiments also shod that the HeightBL algorithm significantly improved range and k-NN query processing performance. It required from 13.38% up to 99.94% less distance calculations and was from 8.57% up to 99.04% faster than the element-by-element insertion.
['Arthur Emanuel de Oliveira Carosia', 'Ricardo Rodrigues Ciferri', 'Cristina Dutra de Aguiar Ciferri']
I am writing a research paper on a idea The HeightBL Algorithm for Bulk-loading F-Onion-trees
[{"from": "human", "value": "I am writing a research paper on a idea The HeightBL Algorithm for Bulk-loading F-Onion-trees"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The F-Onion-tree is a robust access method that slices the metric space into disjoint subspaces to provide quick indexing of complex data in the main memory. Hover, the F-Onion-tree only performs element-by-element insertions into its structure, i.e. it does not introduce a technique to build the index considering all elements of the dataset at once. In this article,  fill this gap.  propose the HeightBL algorithm for bulk-loading F-Onion-trees. Performance tests with real-world data with different volumes and dimensionalities shod that the index produced by the HeightBL algorithm is very compact. Compared with the element-by-element insertion, the size of the index reduced from 53.42% to 71.25%. The experiments also shod that the HeightBL algorithm significantly improved range and k-NN query processing performance. It required from 13.38% up to 99.94% less distance calculations and was from 8.57% up to 99.04% faster than the element-by-element insertion."}]