N.J. Larsson, Structures of String Matching and Data Compression. Ph.D. Dissertation, Dept. of Computer Science, Lund University, Sweden, 1999.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....outside the sliding window. In case of a su#x tree, when a new edge is created, we can guarantee the above regulation by traversing from the leaf node toward the root node while updating all edge labels encountered. However, this would yield quadratic time complexity in the aggregate. Larsson [49, 50] utilized credit issuing, an update number restriction technique, originally proposed in [22] which takes in total O( w ) time and space. In the following, we introduce an extended credit issuing technique for CDAWGs. Our basic strategy is to show that we can handle the credit issuing as well as ....

....in the above paragraph, we traverse the reversed graph rooted at u in width first fashion to update edge labels. In the worst case, the updating cost is proportional to the number of paths from the source node to node u. Nevertheless, it is bounded by DelSize(w) By analogous arguments to [22, 49, 50], we can establish the following lemma. Lemma 15 All edge labels of a CDAWG can be kept valid in a sliding window, in linear time and space with respect to the length of an input string. As a conclusion of Section 6.2, we finally obtain the following. # # and M be the window size. The ....

N. J. Larsson. Structures of String Matching and Data Compression. PhD thesis, Lund University, 1999.

....outside the sliding window. In case of a su#x tree, when a new edge is created, we can guarantee the above regulation by traversing from the leaf node toward the root node while updating all edge labels encountered. However, this would yield quadratic time complexity in the aggregate. Larsson [13, 14] utilized credit issuing, an update number restriction 12 technique, originally proposed in [7] which takes in total O( w ) time and space. In the following, we introduce an extended credit issuing technique for CDAWGs. Our basic strategy is to show that we can handle the credit issuing as well ....

....in the above paragraph, we traverse the reversed graph rooted at u in width first fashion to update edge labels. In the worst case, the updating cost is proportional to the number of paths from the source node to node u. Nevertheless, it is bounded by DelSize(w) By analogous arguments to [7, 13, 14], we can establish the following lemma. Lemma 8. All edge labels of a CDAWG can be kept valid in a sliding window, in linear time and space with respect to the length of an input string. As a conclusion of Section 4, we finally obtain the following. # # and M be the window size. The proposed ....

N. J. Larsson. Structures of String Matching and Data Compression. PhD thesis, Lund University, 1999.

....operators for processing containment queries e#ciently. In the field of data structures and theoretical computer science however, many papers address the problem of solving proximity queries e#ciently, and even the more general class of regular expression matching, also on large archives, see e.g. [37]. We plan to base new physical operators on existing techniques using su#x arrays, comparable to the approach taken in [21] for approximate string matching with q grams. As discussed, complex access structures are better designed as query plans expressed in more basic physical operators than as ....

N. Jesper Larsson. Structures of String Matching and Data Compression. PhD thesis, Department of Computer Science, Lund University, Sweden, 1999.

....Greene [1989] 14] presented a modification to McCreight s approach for a sliding window, where vertices are continuously deleted 90 in (amortized) constant time per character read, where refinements for strict real time implementation are made in Ukkonen [39] see the Ph.D. thesis of Larsson [17] for further references. Others, such as Brent [8] and the patents of [40] and Whiting [1991] 41] employed hashing to find matches in a sliding window. For LZ78 methods, a simple trie can be used to store the dictionary, and matching can be done in constant time per character read by traversing a ....

N.J. Larsson, Structures of String Matching and Data Compression, Ph.D. Thesis, Dept. of Computer Science, Lund University, Sweden, 1999.

.... remote surveillance, we consider a variable bit rate compression schema based on a multiple pass multiplealgorithm strategy in order to take advantages from the application constrains (a certain tolerance to the information loss) using both lossy and lossless compression mechanisms [5] 6] 7] [8]. More formally called #m the compression rate after m interaction of the data compression algorithm, we assume that: #m = g 1 g 2 m = 1 m m 1 m 1 (4) where g 1 and g 2 are constant values. The figure 4 shows a possible curve for #m with g 1 = 0.1 and g 2 = 0.8. The total ....

N. Larsson, "Structures of String Matching and Data Compression", PhD thesis, Dept. of Comp. Science, Lund Univ., 1999.

....Programs There are several compression methods based on construction of simple deterministic grammars, e.g. see [12] The crucial idea used here is to nd the smallest possible set of productions, a dictionary, that generates a source string. In this paper we adopt a recursive pairing [14] scheme that generates relatively small set of productions. Assume that initially a source (to be compressed) string s 2 A , where A is a source alphabet, and dictionary D is empty. At any stage of the compression process alphabet A is extended to A and it is used to encode a current version S ....

N.J. Larsson, Structures of String Matching and Data Compression. Ph.D. Dissertation, Dept. of Computer Science, Lund University, Sweden, 1999.

....4. 1 Suffix tree description Suffix trees are well established in string processing and a good introduction to their use in biology is provided by Gusfield [20] A suffix tree indexes all suffixes of a given string [43, 32, 41] and the tree storage structure lends itself to space optimisations [27, 28]. A suffix tree over a sequence of length n has construction costs of O(n) time and space, and can be searched within O(k m) time, where k is the pattern length and m is the number of pattern occurrences. We show a suffix tree for the string ACATCTTA in Figure 1, with a unique terminator character ....

N. J. Larsson. Structures of String Matching and Data Compression. PhD thesis, Department of Computer Science, Lund University, 1999.

....consider the specific statistical properties of the data at the output of the BWT. They describe six important properties. These considerations lead to modifications of the coding method, which in turn improve the coding e#ciency. Further improvements related to the sorting are presented in [3, 11, 12]. 2 Context Tree Models of Sources Let A be the discrete alphabet of # symbols, # # 2; x k be the first k symbols of the message x n with x i # A; p(x k #) be the probability of the occurrence of x k at the output of the source # and # = #(x n ) x n # A n be a uniquely ....

N.J. Larsson. Structures of String Matching and Data Compression. PhD thesis, Dept. of Comp. Science, Lund Univ., 1999.

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N.J. Larsson, Structures of String Matching and Data Compression. Ph.D. Dissertation, Dept. of Computer Science, Lund University, Sweden, 1999.

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Larsson N. Structures of String Matching and Data Compression. PhD thesis, Dept. of Comp. Science, Lund Univ., 1999.

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N.J. Larsson. Structures of string matching and data compression. PhD thesis, Department of Computer Science, Lund University, 1999

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