Sentence examples for knowledge workload from inspiring English sources

The obstacle for the Grid to be prevalent is the difficulty in using, configuring and maintaining it, which needs excessive IT knowledge, workload, and human intervention.

Individual patient factors (such as preference for primary or secondary care settings and teams), individual staff factors (knowledge, workload, motivation and interest in mental illness or CHD screening) and communication factors could produce inconsistent levels of CHD-related care in either primary or secondary care settings.

The following list presents examples for those external influencing factors: Intervention on the individual level: Staff changes (e.g. reducing IT knowledge), workload of staff (e.g. reducing time for IT use), changes of hospital strategy (e.g. IT is now seen to contribute to competitiveness of the hospital).

The ubiquitous, ill-defined notion of "fatigue" may be used as a proxy for other more specific individual- and system-level factors, including limited experience, limited content or patient-specific knowledge, high workload, and inadequate supervision.

Our findings add criticality of the practice environment to the commonly known challenges to routine use of partograph reported from other contexts, such as insufficient knowledge and workload pressure.

Experimental results for energy prediction demonstrate that we can accurately predict the peak energy consumption of an application on a target platform; whereas, results for energy optimization indicate that with no a priori knowledge of workloads sharing the platform we can save up to 24% of the overall HPC system's energy consumption under benchmarks and real-life workloads.

However, setting the optimal threshold is not simple and typically requires workload knowledge.

Therefore there is a tradeoff between initial performance and time taken to build workload knowledge.

We propose a dynamic load balancing algorithm which assumes no compile time knowledge about the workload parameters.

The experimental results demonstrate that our approach can reduce cost and brown energy usage with efficient utilization of green energy and without a priori knowledge of future workload, availability of renewable energy, and grid electricity prices.

Our system employs an adaptive scheme that automatically adjusts the level of indexing according to the granularity of the incoming queries, without assuming any prior knowledge of the workload.