Ferragina, P., Manzini, G.: Opportunistic data structures with applications. In: Proc. 41st Annual Symposium on Foundations of Computer Science (FOCS). (2000) 390--398  Home/Search   Document Details and Download   Summary   Related Articles   Check

....This means that the index replaces the text, which can hence be deleted. This is an opportunistic scheme, i.e. the index takes less space if the text is compressible. Yet there is a minimum of 8u bits of space which has to be paid independently of the entropy of the text. Ferragina and Manzini [5] presented a different approach to compress the suffix array based on the Burrows Wheeler transform and block sorting. They need 5uH k O log log u oe log oe bits and can answer queries in O(m R log u) time, where H k is the k th order entropy and the formula is valid for any constant ....

P. Ferragina and G. Manzini. Opportunistic data structures with applications. In Proc. 41st IEEE Symp. Foundations of Computer Science (FOCS'2000), pages 390--398, 2000.

....This means that the index replaces the text, which can hence be deleted. This is an opportunistic scheme, i.e. the index takes less space if the text is compressible. Yet there is a minimum of 8u bits of space which has to be paid independently of the entropy of the text. Ferragina and Manzini [6] presented a di erent approach to compress the sux array based on the Burrows Wheeler transform and block sorting. They need 5uH k O log log u log bits and can answer queries in O(m R log u) time, where H k is the k th order entropy and the formula is valid for any constant k. This ....

....representation that includes longest common pre x information, which is able to count the occurrences of P in O(m) time and of traversing the sux tree in O(n log n) time. It needs nH 1 O(n) bits. Its main interest lies in its ability to handle large alphabets, where it is superior to [6]. However, there are older attempts to produce succinct indexes, by K arkk ainen and Ukkonen [14, 13] Their main idea is to use a sux tree that indexes only the beginnings of the blocks produced by a Ziv Lempel compression (see next section if not familiar with Ziv Lempel) This is the only ....

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P. Ferragina and G. Manzini. Opportunistic data structures with applications. In Proc. 41st IEEE Symp. Foundations of Computer Science (FOCS'00), pages 390-398, 2000.

....methods (such as gzip and compress) and is only second to the PPM algorithm. In terms of running time, the BWT is much faster than PPM, but comparable with LZ based algorithms. So far, the major reported work on searching on BWT compressed text are those of Sadakane [11] and Ferragina and Manzini [7], who proposed O(m log u # occ log u) and O(m # occ log u) time algorithms respectively, to locate all # occ occurrences of P in T , where 0 # 1. In [1, 5] methods were reported that can search for exact matches in BWT text in O(m log ) time. In this paper, we provide algorithms ....

....where F [i] is the c th occurrence of # in F ; 3) Generate the original text T , since the rows in M are cyclic rotations of each other, the symbol L[i] cyclically precedes the symbol F [i] in T . That is, L[V [j] cyclically precedes L[j] in T . For the example with mississippi, we will have V = [6 8 9 5 1 7 2 10 11 3 4]. Given V and L, we can generate the original text by iterating with V . This is captured by a simple algorithm: T [u 1 i] L[V [id] #i = 1, 2, u, where V [s] s; and V i 1 [s] V [V [s] 1 s u. BWT based compression. Compression with the BWT is usually ....

Ferragina, P. and Manzini, G. (2000). Opportunistic data structures with applications. Proceedings, 41st IEEE Symposium on Foundations of Computer Science, FOCS'2000.

....of the text, without scanning the entire compressed text, which is unusual in classical compression schemes. 1. 1 Related Work A new trend in the design of advanced indexes for full text searching of documents is represented by compressed sux arrays [6, 18, 19, 20] and opportunistic FM indexes [2, 3], in that they support the functionalities of sux arrays and sux trees, which are more powerful than classical inverted les [4] An ecient combination of inverted le compression, block addressing and sequential search on word based Hu man compressed text is described in [15] They overcome the ....

....Searching takes O(m lg n) time. If we index the Associated Press le using Sadakane s index, we need roughly 1.6 gigabytes of storage, since we no longer have to store the text. However, the index is not as compressible as the text, even though it is still sublinear in the text size. The FM index [2, 3] is a self indexing data structure using 5nH h O n j j lg lg n j j2 j j lg j j bits, while supporting searching in O(m lg 1 n) time, where j j = O(1) It is based on the Burrows Wheeler transform and is the rst to encode the index size with respect to the high order empirical ....

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P. Ferragina, G. Manzini. Opportunistic data structures with applications. In FOCS, 390-398, 2000.

.... the interest in this data structure has been revitalized by its use as a building block for three novel applications: 1) the Burrows Wheeler compression algorithm [3] which is a provably [17] and practically [20] e ective compression tool; 2) the construction of succinct [10, 19] and compressed [7, 8] indexes; the latter can store both the input text and its full text index using roughly the same space used by traditional compressors for the text alone; and (3) algorithms for clustering and ranking the answers to user queries in web search engines [22] In all these applications the ....

P. Ferragina and G. Manzini. Opportunistic data structures with applications. In Proc. of the 41st IEEE Symposium on Foundations of Computer Science, pages 390-398, 2000.

....if e = L[t] This costs O( # ) O(1) time since # is of constant size. To implement the above algorithm, we need to design a scheme to mark rows. One approach is to mark the rows corresponding to text positions having the form 1 i#, where i = 0, 1, u #. Ferragina and Manzini showed in [27] how to make use of a Packet B tree [7] to make it possible to check whether a row is marked in constant time, when # = #(lg u) and the additional space cost is O( bits per input symbol. Besides, since the loop in the operation is executed at most # times, the algorithm costs O(lg u) ....

....O( bits per input symbol. Besides, since the loop in the operation is executed at most # times, the algorithm costs O(lg u) time. To list all the occurrences, we simply need to perform the above operations occ times, which cost O(occ lg ) time, and the space cost remains the same. In [27], Ferragina and Manzini also designed another more complicate method to implement the locate operation in O(lg u) time, at additional space cost of O( bits per input symbol. Index Structures Now we introduce how to organize the index structures except the additional information required ....

P. Ferragina and G. Manzini. Opportunistic data structures with applications. In Proc. of the 41st IEEE Symposium on Foundations of Computer Science, pages 390--398, 2000.

....d. For any row c# of M , where c is its first character and # is the rest of the string, we assume it is the r th row of M starting with c. Then we have the row #c is also the r th row of M ending with c. We also define a mapping among the rows of M , called Last to First mapping, or LF mapping [26]. Given a row ending with a certain character c, we assume it is the i th row of M , where i = 0, 1, u. When we shift the row one character to the right to get a string that starts with c, we assume the new string is the j th row of M . The LF mapping shows the correspondence between such ....

....SASE archives compression ration comparable to that of Gzip, and supports e#cient keyword query. However, the support for query of SASE are limited to that of word indices, especially inverted lists. 4.2. 2 Opportunistic Index Opportunistic data structures were proposed by Ferragina and Manzini [26]. They are called opportunistic because their space occupancy is decreased when the input text is compressible and the space reduction is achieved at no significant slowdown in the query performance [26] Opportunistic indices make use of the fact that the conceptual matrix M stores all the ....

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P. Ferragina and G. Manzini. Opportunistic data structures with applications. Technical Report TR-00-03, University of Pisa, 2000.

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P. Ferragina and G. Manzini. Opportunistic data structures with applications. In Proc. of the 41st IEEE Symposium on Foundations of Computer Science, pages 390--398, 2000.

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P. Ferragina and G. Manzini. Opportunistic data structures with applications. In Proc. of the 41st IEEE Symposium on Foundations of Computer Science, pages 390-398, 2000.

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Paolo Ferragina and Giovanni Manzini. Opportunistic data structures with applications. In IEEE Symposium on Foundations of Computer Science (FOCS '00), pages 390--398, 2000.

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P. Ferragina and G. Manzini. Opportunistic data structures with applications. In Proc. of the 41st IEEE Symposium on Foundations of Computer Science, pages 390--398, 2000.

....e#ciently extract the user requested information. Approaches to combine compression and indexing techniques are nowadays receiving more and more attention. A first step towards the design of a compressed full text index achieving guaranteed performance in the worst case has been recently done in [10]. The novelty of that index resides in the careful combination of the compression algorithm proposed by Burrows and Wheeler [6] with the su#x array data structure [16] The index is opportunistic in that, although no assumption on a particular fixed distribution is made, it takes advantage of the ....

....itself and still support e#ective search operations; however, the space occupancy of their data structures grows linearly with the size of the indexed text. The first step towards the design of a compressed index ensuring e#ective search performance in the worst case has been recently pursued in [10]. The novelty of the approach in [10] resides in the careful combination of the Burrows Wheeler compression algorithm [6] with the su#x array data structure [16] to obtain a sort of compressed su#x array (see Section 2) The resulting index is opportunistic in that, although no assumption on a ....

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P. Ferragina and G. Manzini. Opportunistic data structures with applications. In Proceedings of the 41st IEEE Symposium on Foundations of Computer Science, 2000.

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Ferragina, P., Manzini, G.: Opportunistic data structures with applications. In: Proc. 41st Annual Symposium on Foundations of Computer Science (FOCS). (2000) 390--398

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Ferragina, P. & Manzini, G. (2000), Opportunistic data structures with applications, in `Proceedings of the 41st IEEE Symposium on Foundations of Computer Science (FOCS 00)', IEEE Computer Society, Redondo Beach, CA, pp. 390--398.

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Ferragina, P., Manzini, G.: Opportunistic data structures with applications. In: Proc. 41st Annual Symposium on Foundations of Computer Science (FOCS). (2000) 390--398

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P. Ferragina and G. Manzini, Opportunistic Data Structures with Applications. Proc. 41st IEEE Symposium on Foundations of Computer Science, (FOCS'00). Redondo Beach (CA), 2000, pp. 390--398.

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P. Ferragina and G. Manzini. Opportunistic data structures with applications. In Proc. FOCS'00, pp. 390--398, 2000.

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P. Ferragina and G. Manzini. Opportunistic data structures with applications. In Proc. FOCS'00, pp. 390-398, 2000.

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P. Ferragina and G. Manzini. Opportunistic data structures with applications. In Proc. 41st IEEE Symp. Foundations of Computer Science (FOCS'00), pages 390-398, 2000.

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P. Ferragina and G. Manzini. Opportunistic Data Structures with Applications. In Proc. IEEE Symp. on Foundations of Computer Science (FOCS'00), pp. 390398, 2000.

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P. Ferragina and G. Manzini. Opportunistic Data Structures with Applications. In 41st IEEE Symp. on Foundations of Computer Science, pages 390--398, 2000.

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P. Ferragina, G. Manzini. Opportunistic data structures with applications. In: Proc. 41st Annual Symposium on Foundations of Computer Science. 12--14 November, 2000, Redondo Beach, California, pp. 390--398. IEEE Computer, NewYork

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P. Ferragina and G. Manzini. Opportunistic Data Structures with Applications. In 41st IEEE Symp. on Foundations of Computer Science, pages 390--398, 2000.

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P. Ferragina and G. Manzini. Opportunistic data structures with applications. In 41st Ann. Symp. on Found. of Comput. Sc., pages 390-398. IEEE Comput. Soc. Press, Los Alamitos, CA, 2000.

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P.Ferragina and G. Manzini. Opportunistic data structures with applications. In IEEE Symposium on Foundations of Computer Science, pages 390#398, 2000.

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