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引用次数: 3
摘要
我们提出了用于区分大型分层数据集的外部存储器算法。我们的方法特别适合具有有限差异的流数据。对于输入大小m和n以及最大输出(差异)大小e，我们的算法rdiff的I/O、RAM和CPU成本分别为m + n、4e + 8和O(MN)。也就是说，给定4e + 8块RAM，我们的算法除了读取两个输入所需的操作外，不执行任何I/O操作。我们还提出了该算法的一种变体，它只使用4块RAM, I/O成本为8me + 18m + n + 6e + 5, CPU成本为O(MN)。
We present external-memory algorithms for differencing large hierarchical datasets. Our methods are especially suited to streaming data with bounded differences. For input sizes m and n and maximum output (difference) size e, the I/O, RAM, and CPU costs of our algorithm rdiff are, respectively, m + n, 4e + 8, and O(MN). That is, given 4e + 8 blocks of RAM, our algorithm performs no I/O operations other than those required to read both inputs. We also present a variant of the algorithm that uses only four blocks of RAM, with I/O cost 8me + 18m + n + 6e + 5 and CPU cost O(MN).
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