广义广义逆：界、收敛和渐近性质
Generalized Deduplication: Bounds, Convergence, and Asymptotic Properties
摘要：
我们研究了重复数据删除的泛化，它可以对高度相似的数据进行无损重复数据删除，并表明具有固定块长度的标准重复数据删除是一种特殊情况。我们提供了广义重复数据删除的编码序列的预期长度的界限，并表明编码具有渐近近熵成本下提出的源模型。更重要的是，我们发现，广义重复数据删除允许多个数量级的收敛速度比标准的重复数据删除。这意味着广义重复数据删除可以比标准重复数据删除更早地提供压缩优势，这在实际系统中是关键的。数值例子证明了我们的结果，表明我们的下限是可以实现的，并说明了使用标准重复数据删除的泛化的潜在收益。事实上，我们表明，即使是一个简单的情况下，广义重复数据删除，收敛速度的增益是线性的数据块的大小。
英文摘要：
We study a generalization of deduplication, which enables lossless deduplication of highly similar data and show that standard deduplication with fixed chunk length is a special case. We provide bounds on the expected length of coded sequences for generalized deduplication and show that the coding has asymptotic near-entropy cost under the proposed source model. More importantly, we show that generalized deduplication allows for multiple orders of magnitude faster convergence than standard deduplication. This means that generalized deduplication can provide compression benefits much earlier than standard deduplication, which is key in practical systems. Numerical examples demonstrate our results, showing that our lower bounds are achievable, and illustrating the potential gain of using the generalization over standard deduplication. In fact, we show that even for a simple case of generalized deduplication, the gain in convergence speed is linear with the size of the data chunks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
