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This section has outlined an end to end, open source proof of concept solution to transform, process, analyse and identify resolutions to similar technical support calls given an open case.
An end to end proof of concept solution based entirely on open source components is described that can be used to process and analyse large technical support datasets to categorise similar technical support calls and identify likely resolutions.
Thus this paper;s contributions are two-fold: An end to end proof of concept solution based entirely on open source components is described that can be used to process and analyse large technical support datasets to categorise similar technical support calls and identify likely resolutions.
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While our previous work has demonstrated proof-of-concept solutions, it lacked fundamental communication abstractions, a rigorous design, and a thorough analysis on the effectiveness of these solutions.
The challenges, if properly addressed, could be a step from the concept of 5G networks to proof-of-concept solutions which provide E2E user's security based on slices isolation.
For the purpose of this paper, the collection of individual components that represent mOSAIC's proof-of-concept prototype solutions are depicted in Figure 1.
Although the device efficiency is too low for commercialization, these devices provide a proof-of-concept that solution-deposited CIGS nanocrystal films can function in PV devices, enabling unconventional device architectures and materials combinations, including the use of flexible, inexpensive and light-weight plastic substrates.
Proof-of concept experimental results are provided.
A proof-of-concept enabling multiple formulation solution hosting (including combinatorial grading) in a single centrifugal electrospinning system (CES) comprising one spinneret is shown.
A prototype system has been developed for this proof-of-concept, using open platform solutions including MapReduce, Hadoop Distributed File System (HDFS), and a machine-learning tool.
Future work includes performing a proof-of-concept validation, by implementing the solution and conducting tests using real vehicles.
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