Sentence examples for high degree of data from inspiring English sources

A performance-based quality assurance/quality control (QA/QC) approach was developed to ensure a high degree of data quality, consistency and comparability.

Since large-scale data intensive applications frequently involve a high degree of data access locality, the DSSM divides GOS nodes into multiple geographically distributed domains to facilitate the locality and simplify the intra-domain storage management.

Applications that involve a high degree of data parallelism such as database management, text processing, image processing, graph processing, bioinformatics, weather modeling, managing UAS Unmanned Aircraft Systemss or drones) etc., are good candidates for AP solutions.

On the other hand, general purpose computing using Graphics Processing Units (GPUs) has become a very active field because of the high speed-up ratios that can be obtained when applied to problems that exhibit a high degree of data parallelism.

Big Data storage management is indeed among the most important challenges for computing environments since many data intensive applications usually involve a high degree of data access locality.

"We bring a high degree of data and underlying prediction intelligence to bear, to know which users to go after in their native environment – be it phone or tablet, iOS or Android," says Ellis. "Customers pay us on more of a cost-per-action rather than a cost-per-install basis".

Therefore, our improved method can relatively quickly determine the structure with highest degree of data matching.

Ancker et al. observed that "secondary use of data requires a generally higher degree of data integrity than required for the original primary use" [ 25].

Several important milestones have been reached recently towards the goal of a higher degree of data and information exchange among providers and consumers.

This strategy turns out to introduce a hidden bias towards sick patients because sicker patient have records with a higher degree of data sufficiency and are more likely to be included in EHR-based studies; therefore, this selection process biases the study populations towards those comprised of sicker patients.

We excluded studies that had a high degree of incomplete data (defined as having more than 40% incomplete data) during the risk of bias assessment or when it appeared that the missing data were likely to be associated with the reported intervention effect.