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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].
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For a batch of query points, the main idea is to use previous queries to improve the current one.
Furthermore, estimates of the prediction time for a batch of query domain architectures are shown in Figure 6.
Recently, there have been interesting studies that apply MQO to MapReduce frameworks for unstructured data; for example MRShare [40] processes a batch of input queries as a single query.
A batch of SQL queries was executed on the source relational database to extract this experimental subset of the EHR dataset with timestamps between June 2005 and December 2005.
Instead of focusing on a single drug target, using a batch of genes to query the Connectivity Map (Cmap, http://www.broad.mit.edu/cmap/) may not only allow multiple targets to be considered simultaneously, but it may also identify potential new drugs.
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.
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.
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