对于数据集规模过大而无法一次性完成粒度排序的问题亦是很好的解决方案。
The incremental algorithm is also a good solution to the problem of one-time granular ranking in large-scale databases.
在最后的部分中，我们通过范例显示了中间值和标准偏差对于数据集内的分散度都没有多大的抵抗力。
In the final section we showed by example that both the mean and standard deviation are less resistant to dispersion within a dataset.
问题是，对于许多想要设计系统来应对这些挑战的研究人员来说，实验所需的大量数据集根本不存在。
The trouble is, for many of the researchers who'd like to design systems to address these challenges, massive datasets for experimentation just don't exist.
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