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The effect of the correlation of service processes in both systems in the fixed-point methodology is taken into account by the introduction of the notion of effective traffic.
The spatiotemporal correlation of service usages for user i is fully characterized by the number of events corresponding to the service s in the cell c and at time t, denoted by n i s ( c, t ).
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Figure 8 Contextual correlations of service usages.
Contextual correlations of service usages are measured for users 5 and 81 and for all users (from top to bottom).
Our findings on the spatiotemporal correlations of service usage patterns for different contexts enable us to better understand the behavior of humans and what that implies.
We turn to analyze the temporal correlations of service usages in terms of n i s ( t ) = ∑ C n i s ( C, t ), where the summation is over all contexts with one exception, Abroad.
For gaining contextual understanding of correlations we consider the contexts instead of cells, i.e. n i s ( C, t ) = ∑ c n i s ( c, t ), where the summation is over c detected as context C. We first focus on the contextual correlations of service usages with n i s ( C ) = ∑ t n i s ( C, t ).
BFI Chief Executive Bruce E. Ranck, for: "Our new organization facilitates the correlation of our services with what our customers tell us they want and need".
In this paper, we study spatiotemporal correlations of the service usage patterns of individual users by analyzing a handset-based dataset.
In an extensive survey of tipping studies, Michael Lynn, a professor at Cornell, found only a weak correlation between the quality of service that people report receiving and the tips they give.
The probability of many of these medical conditions increases with age and age was found to be associated with length of service (correlation = 91.4%).
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