Research Article Open Access

Highly-Available, Collaborative, Trainable Communication – a Policy – Neutral Approach

Christian Schreibaumer1, Isabella Stein1 and Eberhard Dobermann1
  • 1 University of Applied Sciences of Lower Saxony at Himmelpforten, Germany
Journal of Computer Science
Volume 14 No. 6, 2018, 747-752

DOI: https://doi.org/10.3844/jcssp.2018.747.752

Submitted On: 20 December 2017 Published On: 5 April 2018

How to Cite: Schreibaumer, C., Stein, I. & Dobermann, E. (2018). Highly-Available, Collaborative, Trainable Communication – a Policy – Neutral Approach. Journal of Computer Science, 14(6), 747-752. https://doi.org/10.3844/jcssp.2018.747.752

Abstract

Scalable theory have led to many advances. Data flow is constantly growing and systems are expanding. Theoretical principles of red-black trees can help to build a scalable system, where data easily can expand and in the end energy is saved. We propose a novel solution, an organizational platform, an algorithm for the analysis of agents, which we call Mop. With experimental results we show, that Mop is faster than the Apriori or any other algorithm concerning scalable theory. Mop is even faster than the ADFD-growth algorithm, especially when tested in a very low key RAM environment.

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Keywords

  • Cryptoanalysis
  • Machine Learning
  • Algorithms
  • IPv7