Redundancy elimination within large collections of files. Kulkarni, P.; Douglis, F.; Lavoie, J.; and Tracey, J. M. 2004.
Redundancy elimination within large collections of files [link]Paper  abstract   bibtex   
Ongoing advancements in technology lead to ever-increasing storage capacities. In spite of this, optimizing storage usage can still provide rich dividends. Several techniques based on delta-encoding and duplicate block suppression have been shown to reduce storage overheads, with varying requirements for resources such as computation and memory. We propose a new scheme for storage reduction that reduces data sizes with an effectiveness comparable to the more expensive techniques, but at a cost comparable to the faster but less effective ones. The scheme, called Redundancy Elimination at the Block Level (REBL), leverages the benefits of compression, duplicate block suppression, and delta-encoding to eliminate a broad spectrum of redundant data in a scalable and efficient manner. REBL generally encodes more compactly than compression (up to a factor of 14) and a combination of compression and duplicate suppression (up to a factor of 6.7). REBL also encodes similarly to a technique based on delta-encoding, reducing overall space significantly in one case. Furthermore, REBL uses super-fingerprints, a technique that reduces the data needed to identify similar blocks while dramatically reducing the computational requirements of matching the blocks: it turns O(n2) comparisons into hash table lookups. As a result, using super-fingerprints to avoid enumerating matching data objects decreases computation in the resemblance detection phase of REBL by up to a couple orders of magnitude.
@conference {1247420,
	title = {Redundancy elimination within large collections of files},
	booktitle = {ATEC {\textquoteright}04: Proceedings of the annual conference on USENIX Annual Technical Conference},
	year = {2004},
	pages = {5{\textendash}5},
	publisher = {USENIX Association},
	organization = {USENIX Association},
	address = {Berkeley, CA, USA},
	abstract = {Ongoing advancements in technology lead to ever-increasing storage capacities. In spite of this, optimizing storage usage can still provide rich dividends. Several techniques based on delta-encoding and duplicate block suppression have been shown to reduce storage overheads, with varying requirements for resources such as computation and memory. We propose a new scheme for storage reduction that reduces data sizes with an effectiveness comparable to the more expensive techniques, but at a cost comparable to the faster but less effective ones. The scheme, called Redundancy Elimination at the Block Level (REBL), leverages the benefits of compression, duplicate block suppression, and delta-encoding to eliminate a broad spectrum of redundant data in a scalable and efficient manner. REBL generally encodes more compactly than compression (up to a factor of 14) and a combination of compression and duplicate suppression (up to a factor of 6.7). REBL also encodes similarly to a technique based on delta-encoding, reducing overall space significantly in one case. Furthermore, REBL uses super-fingerprints, a technique that reduces the data needed to identify similar blocks while dramatically reducing the computational requirements of matching the blocks: it turns O(n2) comparisons into hash table lookups. As a result, using super-fingerprints to avoid enumerating matching data objects decreases computation in the resemblance detection phase of REBL by up to a couple orders of magnitude.
},
	url = {http://portal.acm.org/citation.cfm?id=1247420$\#$},
	author = {Kulkarni, Purushottam and Douglis, Fred and Jason Lavoie and Tracey, John M.}
}
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