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Although compression-based data reduction methods are feasible for big data reduction, the processing overhead of de-compression introduces latency which lowers the performance of analytics algorithms in real-time environments.
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Aiming to tackle these challenges and to enhance the performance of analytic query processing, the concept of data warehouse [13] and OLAP [6] have been introduced.
Performance of analytic solution is tested against numerical solution of the nonlinear Boussinesq equation at 200 equally spaced grid points of length 1 m for t = 2, 5, 10, 20, and 50 days.
We have evaluated the accuracy of the estimations of our method during the performance of analytic rehabilitation exercises of the wrist and elbow.
Consequently, the performance of the analytic approximation (11) is not fully satisfying either, since the analytic approximation was derived under the assumption that the electrodes are placed in a large medium.
This allows one to empirically evaluate performance characteristics of analytic methods by determining the frequency with which a method obtains the known right answers.
The performance of big data analytics application is related to the characteristics of jobs and the configuration of clusters, which have a direct impact on performance of big data analytics applications.
For this reason, the performance of traditional data analytics will be limited in solving the volume problem of big data.
What is the best way to control TCP flows on mobile devices for improving performance of mobile distributed analytics?
As a result, the performance of traditional data analytics may not be useful to the problem of velocity problem of big data.
The performance of mobile distributed analytics largely relies on how effectively every mobile device utilizes the available network resource through TCP connections.
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