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However, in many data stream scenarios data distribution across different windows can be quite different.
In such scenarios, data service performance assessment is usually addressed through active terminal monitoring over real networks [3].
In order to adapt to different application scenarios, data extraction can be done either in the encrypted domain or in the decrypted domain.
Different data arrival patterns under various scenarios: Data in routine health monitoring are generated periodically where the data arrival rate can be determined by the sampling rate uniquely.
Moreover, in many real-world scenarios data is not categorised in predefined topics, but it is generally accompanied by semantic descriptions possibly describing users' interests.
Principles and application of ITC have been well documented over recent years, experimentally the technique is simple to use and in ideal scenarios data analysis is trivial.
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To assess the impact of prior knowledge, we ran kDDN under three scenarios: data-only (kDDN.dt), data plus true prior knowledge (kDDN.tk), and data plus "random" prior knowledge (kDDN.fk).
Therefore, we combined the scenario data to analyze each hypothesis.
In the federative scenario, data manipulation is limited to the federative SELECT statement.
The scenario data are generated by STARS II [[39]] and interpolated to the same grid.
Since, there are bound to be numerous errors in this scenario, data estimation suffers.
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