BIRCH algorithm is a clustering algorithm for very large datasets.
BIRCH算法是针对大规模数据集的聚类算法。
Data generators are used to populate tables with random test data, which is especially helpful when very large datasets are needed.
数据生成器使用随机的测试数据来填充数据表，当需要大量数据集的时候这个功能特别有用。
For very large datasets, it's hands down the fastest format, beating out even natively executed JSON in parse speed and overall load time.
对于非常大的数据集，它是最快的传输格式，甚至可以在解析速度和下载时间上击败本机执行的JSON。
While Mnesia excels at scalability and low latency in transactions on horizontally fragmented data, one remaining challenge may be how it will scale in terms of very large datasets.
对于横向分片数据，Mnesia在伸缩性和低延迟事务上表现突出，接下来的一个挑战可能是对于超大规模数据集它如何伸展。
XQuery was designed for extracting data from potentially very large XML datasets.
XQuery的设计目标是从可能非常大的XML数据集中提取数据。
It replaced the original indexing algorithms and heuristics in 2004, given its proven efficiency in processing very large, unstructured datasets.
它取代了2004开始试探的最初索引算法，它已经证明在处理大量和非结构化数据集时更有效。
These datasets can grow very large, very quickly and this service is designed to keep it all manageable.
这些数据集可能会变得非常大，非常迅速，这项服务的目的是保持它的可控性。
That would be a very efficient query in both architectures, with relational performing better much better with small datasets but less so with a large dataset.
这将是在这两种体系结构非常有效的查询，与关联效果更好更小的数据集但得到的大量数据没有那么好。
That would be a very efficient query in both architectures, with relational performing better much better with small datasets but less so with a large dataset.
这将是在这两种体系结构非常有效的查询，与关联效果更好更小的数据集但得到的大量数据没有那么好。
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