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The results of this simulation show that Andorra-I exhibits reasonable running time performance, but it does not scale well.
Although the notation is effective in delivering a clear visual representation of set theoretical relationships, it does not scale well.
Though it does not scale well in large network scenario yet it provides an estimate of the optimal values of performance metrics achievable under a specific scenario.
Such an architecture, however, can be impractical when data are not centrally located, it does not scale well to very large datasets, and introduces single-point of failure risks which could compromise the integrity and privacy of the data.
However, it does not scale well with increasing index sizes and query traffic volumes because queries are evaluated on the entire web index, which has to be replicated and maintained in all data centers.
Although OCL is a powerful language to perform queries over MOF models, it has two main problems: (i) it does not scale well when building complex queries [34] and (ii) changing complex queries may induce to developers in making mistakes.
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But it doesn't scale well.
That's because you've been running these queries on CPUs and it doesn't scale well.
Unfortunately, it doesn't scale well in complex applications.
Furthermore, despite being faster than IDP, it still does not scale well for real problems.
However, while this method provides accurate estimates of functional similarity based on GO annotation similarity, it is pairwise and does not scale well to large gene sets.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com