To accurately map each NLP query to an equivalent SQL query, we leverage a high level conceptual description of an application in the context of which all NLP queries are interpreted.
Instead of re-mapping metagenomic reads for each query, reads were mapped once to the gene catalog, which can then be queried many times.
The gene ontology (GO) terms associated with each BLAST hit were mapped and assigned to each query (E-value cutoff = 1 × 10−6, annotation cutoff = 55, GO weight = 5).
The optimal assignment step maps each atom of the query compound onto an atom of the data set compound.
The optimal assignment maps each atom of the query onto an atom of the library compound.
We consider four properties of mappings: mapping satisfiability, mapping inference, query answerability and mapping losslessness.
When MapObjects controls are integrated with an Excel sheet, they have the ability to save and show maps, query addresses, and carry out other interactive functions.
This module (Susceptibility/Hazard and Risk Maps Query and Retrieval Engine in Fig. 1) makes use of the SDI catalogue service to manage, query, and retrieve the metadata.
MEGAN [ 8] maps query sequences to the NCBI nr database using BLAST, and assigns them taxonomic labels according to the lowest common ancestor of the top database hits.
In effect, a MapReduce algorithm maps both the query and the dataset into constituent parts.
