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Reducing the Space Requirement of Suffix Trees
 Software – Practice and Experience
, 1999
"... We show that suffix trees store various kinds of redundant information. We exploit these redundancies to obtain more space efficient representations. The most space efficient of our representations requires 20 bytes per input character in the worst case, and 10.1 bytes per input character on average ..."
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Cited by 131 (12 self)
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We show that suffix trees store various kinds of redundant information. We exploit these redundancies to obtain more space efficient representations. The most space efficient of our representations requires 20 bytes per input character in the worst case, and 10.1 bytes per input character on average for a collection of 42 files of different type. This is an advantage of more than 8 bytes per input character over previous work. Our representations can be constructed without extra space, and as fast as previous representations. The asymptotic running times of suffix tree applications are retained. Copyright © 1999 John Wiley & Sons, Ltd. KEY WORDS: data structures; suffix trees; implementation techniques; space reduction
Partial Fillup and Search Time in LC Tries ∗
"... Andersson and Nilsson introduced in 1993 a levelcompressed trie (in short: LC trie) in which a full subtree of a node is compressed to a single node of degree being the size of the subtree. Recent experimental results indicated a “dramatic improvement ” when full subtrees are replaced by “partially ..."
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Andersson and Nilsson introduced in 1993 a levelcompressed trie (in short: LC trie) in which a full subtree of a node is compressed to a single node of degree being the size of the subtree. Recent experimental results indicated a “dramatic improvement ” when full subtrees are replaced by “partially filled subtrees”. In this paper, we provide a theoretical justification of these experimental results showing, among others, a rather moderate improvement of the search time over the original LC tries. For such an analysis, we assume that n strings are generated independently by a binary memoryless source (a generalization to Markov sources is possible) with p denoting the probability of emitting a “1 ” (and q =1 − p). We first prove that the so called αfillup Fn(α) (i.e., the largest level in atriewithαfraction of nodes present at this level) is concentrated on two values whp (with high probability); either Fn(α) = kn or Fn(α) = kn + 1 where kn =log 1 √pq n −  ln(p/q) 2ln3/2 (1 / √ pq) Φ−1 (α) √ ln n + O(1) is an integer and Φ(x) denotes the normal distribution function. This result directly yields the typical depth (search time) Dn(α) intheαLC tries with p ̸ = 1/2, namely we show that whp Dn(α) ≈ C1 log log n where C1 = 1/  log(1 − h / log(1 / √ pq))  and h = −p log p − q log q is the Shannon entropy rate. This should be compared with recently found typical depth in the original LC tries which is C2 log log n where C2 =1/  log(1 − h / log(1 / min{p, 1−p})). In conclusion, we observe that α affects only the lower term of the αfillup level Fn(α), and the search time in αLC tries is of the same order as in the original LC tries. 1
PAPER Analysis and Improvement of ContentAware Routing Mechanisms
, 2005
"... SUMMARY Over the past few years, there has been significant interest in contentaware routing that use the information found in the payload of packets to provide intelligent request distribution. As these contentaware routing mechanisms have become an increasingly important building block for Inter ..."
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SUMMARY Over the past few years, there has been significant interest in contentaware routing that use the information found in the payload of packets to provide intelligent request distribution. As these contentaware routing mechanisms have become an increasingly important building block for Internet service providers, the network behavior and effectiveness of such mechanisms are unclear. In this paper we analyze the network dynamic of a busy Web site with the contentaware routing mechanism. We find that some unique characteristics of Web traffic may limit the effectiveness of the contentaware switching. Based on these observations, we also propose solutions to remedy these deficiencies. key words: contentaware routing, network dynamic, web traffic 1.