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The authors compute the value of each relation for 6 million users and compare them.
In such systems, tuples of each relation in the database were partitioned (declustered) across disk storage units attached directly to each processor.
The conversion depends on the subtype of each relation.
The database Δ in Figure 1 contains four such relations wherein the primary key of each relation is underlined.
The type constraints of each relation are checked against a configuration file; arbitrary relations are not allowed.
The value concept mapping function φ is implemented as σ A = c (π A (r)) for each attribute A of each relation r in Δ.
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The modeling of semantic relation types by assigning sets of verbs to each relation type renders this approach as highly generalizable and flexible.
For each user, the algorithm starts by counting the number of occurrences for each relation or interaction pattern.
In our approach, the deviation of the Cα coordinates of each residue from each relation is calculated.
Table 3 shows the strength of each independent relation and the level of significance.
The final covariate model was achieved when deletion of each covariate relation was significant.
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