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However, the analysis of many real data intensive applications reveals that jobs and applications request groups of correlated files.
However, because not many real data points are discarded using DFBETA (cf. fraction of false positives in Figure S3), we consider this effect unproblematic.
This cannot be said of the available implementation of the GLS method: firstly, for many real data sets the test statistic cannot be calculated.
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Clustering real-world data is a challenging task, since many real-data collections are characterized by an inherent sparseness and variable distribution.
Figures 6 and 7 demonstrate that for networks with heavily skewed degree distributions (e.g., scale-free networks and many real-data examples) underprediction of existing links may mislead a planner to overspend on seeding to achieve target cascade sizes.
Our initial experiments expose that budgets planned based on (G') in networks with heavily skewed degree distributions (as in many real-data examples) can lead to wasteful levels of seeding.
However, while many real survival data meet the assumption of proportional hazards, this assumption does not generally hold.
Consequently, risk and cost data used to support climate adaptation is of high uncertainty and in many occasions, real data is often unavailable and incomplete.
ZDB architecture optimizes the index of key-value store for auto-incremental integer keys which can be applied in storing many real-life data efficiently with minimize memory overhead and reduce the complexity for partitioning data.
However, many real-world data are generated dynamically.
Recently, transfer learning techniques have been applied successfully in many real-world data processing applications, such as cross-domain text classification, constructing informative priors, and large-scale document classification [55 57]. 5.
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