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The implementation of the prediction model is also discussed.
In this section, the concept and the implementation of the prediction algorithm is discussed.
The required components to conduct this experiment are: a benchmark to generate the workload patterns, an infrastructure to deploy the benchmark, and an implementation of the prediction algorithms.
The implementation of the prediction and configuration technique covers different parts – off-line and on-line components: The pattern determination and the construction of the patterns-quality matrix is done off-line based on monitoring logs.
An online implementation of the prediction method presented in this article is available at http://calpain.org.org
Availability: A Java implementation of the prediction methods and supplementary material are available at http://www.bio.ifi.lmu.de/Complexes/Substructures/.
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We are currently planning to use this implementation for the prediction of targets for differentially expressed microRNAs in children affected with Kawasaki disease and controls and have made our code and examples available so that others could use on their data as well.
We also discuss the conditions under which a simple implementation of the optimal prediction method can be expected to perform well.
The proposed algorithm is based on an efficient parallel implementation of the inter prediction procedure for both P and B-frames involved in the ME and DE processes, and also includes a mechanism to obtain an accurate estimation of how movement takes place (MVP) depending on whether the MB is temporary or inter-view predicted.
We have developed an online implementation of the full prediction model for LQTS: APPRAISE: Assessing Pathogenicity PRobAbility by Integrating Statistical Evidence [ 34].
For a more in-depth explanation of the implementation of conformal prediction, we refer the reader to a recent study by Norinder et al. [26].
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