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PhyloFinder uses an inverted index to achieve fast querying.
This approach provides fast querying over joined data sets, but also requires continuous updating.
BioR can also perform searches on identifiers that can be indexed using a BioR toolkit command for fast querying.
We represent the electronic components by graphs and we evaluate, on one hand, different structures useful to encode a set of graphs, and on the other hand, indexing structures for fast querying of these graphs.
The use of the UC_STRUCTURE and UC_FIKHB_HIERARCHY tables working together in this way allows for very fast querying, but retrieves only whole InChIs, and not the separate InChI components of multiple component InChIs, and the separate layers of the single InChIs, required for comparative purposes.
To this end, we provide a system allowing for fast querying in the order of hundreds of thousands of features.
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Queries have been divided into three categories: slow queries with a slow pronunciation speed (above 5.90 hundredth of seconds per phone), medium queries with a medium pronunciation speed (between 5.90 and 4.82 hundredth of seconds per phone), and fast queries with a fast pronunciation speed (below 4.82 hundredth of seconds per phone).
This fact leads to some simplifications for the implementation of the index and allows faster querying time.
We designed a key-value [ 18] based data model to support faster queries over large-scale microarray data, and implemented the schema using HBase.
Facilitated by a cloud environment, HCBL allows fast queries from remote modelers.
Additionally, to facilitate fast queries specifically for deforming meshes, we propose a novel algorithm for the bottom-up construction of BVHs that results in much faster distance queries.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com