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From the literature discussed in "Related work" and "Traditional heuristic approaches" sections, it can be stated that we need to preserve the data privacy in both ways i.e. for secure data storage/rule mining at a third party as well as by masking restricted sensitive information before data sharing.
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"Sampling approaches" section describes the proposed method for Twitter sampling.
(For a detailed discussion, see the entry on moral psychology: empirical approaches, section 4).
The details of proposed approaches are described in The proposed management approaches section.
"Hybrid auto-scaling approaches" section addresses work that highlights auto-scaling performed via hybrid approaches.
"General auto-scaling approaches" section discusses general auto-scaling approaches, whereas "Resource management frameworks" section discusses resource management frameworks.
First, we present the main results regarding the normal hearing auditory model in comparison to the reference approaches (Section 6.1).
Among collapse approaches (Section 3.1), we shall discuss von Neumann's collapse postulate and theories of spontaneous localisation (for which see also the entry on collapse theories).
For a fuller discussion see the article in this encyclopedia by Stich and Doris, Moral Psychology: Empirical Approaches, Section 6. 35.
Section 5 presents single memoryless source approaches, Section 6 handles Markov-based models, while Section 7 presents linear statistical regression based prediction.
In order to retrieve historical Twitter data, two sets of Twitter users were collected using the random and activity-based sampling as discussed in "Sampling approaches" section.
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