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For a batch of query points, the main idea is to use previous queries to improve the current one.
The program is the more efficient, the larger the batch of query sequences is.
Furthermore, estimates of the prediction time for a batch of query domain architectures are shown in Figure 6.
It should also be noted that processing a large batch of query compounds can become quite time-consuming, particularly for the relatively slow calculation using the MCS metric.
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Executing common tasks only once can remarkably reduce the total execution time of a batch of queries.
Batch mode active learning selecting a batch of queries instead of a single one before retraining is useful when computational time for training is critical.
It transforms a batch of queries into a new batch that will be executed more efficiently by merging jobs into groups and evaluating each group as a single query.
From this perspective, it is important to note that there has been no study, to date, that incorporates the Multiple-query optimization (MQO) technique for Hive to reduce the total execution time of a batch of queries [16] [18].
Each map task then runs a modified implementation of the serial MSPolygraph code, which matches the local batch of queries against the entire database, and also against the spectral library if run on the hybrid mode.
A second set of these workflows provides less detailed target predictions for batches of query compounds, which are uploaded into an SDF Reader node.
Using a method inspired by adaptive mesh refinement, the heuristic iteratively issues batches of queries to be executed in parallel based on past information.
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