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47
Succinct indexable dictionaries with applications to encoding kary trees and multisets
 In Proceedings of the 13th Annual ACMSIAM Symposium on Discrete Algorithms (SODA
"... We consider the indexable dictionary problem, which consists of storing a set S ⊆ {0,...,m − 1} for some integer m, while supporting the operations of rank(x), which returns the number of elements in S that are less than x if x ∈ S, and −1 otherwise; and select(i) which returns the ith smallest ele ..."
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Cited by 191 (7 self)
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We consider the indexable dictionary problem, which consists of storing a set S ⊆ {0,...,m − 1} for some integer m, while supporting the operations of rank(x), which returns the number of elements in S that are less than x if x ∈ S, and −1 otherwise; and select(i) which returns the ith smallest element in S. We give a data structure that supports both operations in O(1) time on the RAM model and requires B(n,m)+ o(n)+O(lg lg m) bits to store a set of size n, where B(n,m) = ⌈ lg ( m) ⌉ n is the minimum number of bits required to store any nelement subset from a universe of size m. Previous dictionaries taking this space only supported (yes/no) membership queries in O(1) time. In the cell probe model we can remove the O(lg lg m) additive term in the space bound, answering a question raised by Fich and Miltersen, and Pagh. We present extensions and applications of our indexable dictionary data structure, including: • an informationtheoretically optimal representation of a kary cardinal tree that supports standard operations in constant time, • a representation of a multiset of size n from {0,...,m − 1} in B(n,m+n) + o(n) bits that supports (appropriate generalizations of) rank and select operations in constant time, and • a representation of a sequence of n nonnegative integers summing up to m in B(n,m + n) + o(n) bits that supports prefix sum queries in constant time. 1
Compressed suffix arrays and suffix trees with applications to text indexing and string matching (extended abstract
 in Proceedings of the 32nd Annual ACM Symposium on the Theory of Computing
, 2000
"... Abstract. The proliferation of online text, such as found on the World Wide Web and in online databases, motivates the need for spaceefficient text indexing methods that support fast string searching. We model this scenario as follows: Consider a text T consisting of n symbols drawn from a fixed al ..."
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Cited by 189 (17 self)
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Abstract. The proliferation of online text, such as found on the World Wide Web and in online databases, motivates the need for spaceefficient text indexing methods that support fast string searching. We model this scenario as follows: Consider a text T consisting of n symbols drawn from a fixed alphabet Σ. The text T can be represented in n lg Σ  bits by encoding each symbol with lg Σ  bits. The goal is to support fast online queries for searching any string pattern P of m symbols, with T being fully scanned only once, namely, when the index is created at preprocessing time. The text indexing schemes published in the literature are greedy in terms of space usage: they require Ω(n lg n) additional bits of space in the worst case. For example, in the standard unit cost RAM, suffix trees and suffix arrays need Ω(n) memory words, each of Ω(lg n) bits. These indexes are larger than the text itself by a multiplicative factor of Ω(lg Σ  n), which is significant when Σ is of constant size, such as in ascii or unicode. On the other hand, these indexes support fast searching, either in O(m lg Σ) timeorinO(m +lgn) time, plus an outputsensitive cost O(occ) for listing the occ pattern occurrences. We present a new text index that is based upon compressed representations of suffix arrays and suffix trees. It achieves a fast O(m / lg Σ  n +lgɛ Σ  n) search time in the worst case, for any constant
Succinct Representation of Balanced Parentheses, Static Trees and Planar Graphs
, 1999
"... We consider the implementation of abstract data types for the static objects: binary tree, rooted ordered tree and balanced parenthesis expression. Our representations use an amount of space within a lower order term of the information theoretic minimum and support, in constant time, a richer set ..."
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Cited by 142 (9 self)
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We consider the implementation of abstract data types for the static objects: binary tree, rooted ordered tree and balanced parenthesis expression. Our representations use an amount of space within a lower order term of the information theoretic minimum and support, in constant time, a richer set of navigational operations than has previously been considered in similar work. In the case of binary trees, for instance, we can move from a node to its left or right child or to the parent in constant time while retaining knowledge of the size of the subtree at which we are positioned. The approach is applied to produce succinct representation of planar graphs in which one can test adjacency in constant time. Keywords: abstract data type, succinct representation, binary trees, balanced parenthesis, rooted ordered trees, planar graphs. AMS subject classifications: 68P05, 68Q65 1 Introduction The binary tree is among the most fundamental of data structures. While it is often the c...
Compressed representations of sequences and fulltext indexes
 ACM Transactions on Algorithms
, 2007
"... Abstract. Given a sequence S = s1s2... sn of integers smaller than r = O(polylog(n)), we show how S can be represented using nH0(S) + o(n) bits, so that we can know any sq, as well as answer rank and select queries on S, in constant time. H0(S) is the zeroorder empirical entropy of S and nH0(S) pro ..."
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Cited by 110 (62 self)
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Abstract. Given a sequence S = s1s2... sn of integers smaller than r = O(polylog(n)), we show how S can be represented using nH0(S) + o(n) bits, so that we can know any sq, as well as answer rank and select queries on S, in constant time. H0(S) is the zeroorder empirical entropy of S and nH0(S) provides an Information Theoretic lower bound to the bit storage of any sequence S via a fixed encoding of its symbols. This extends previous results on binary sequences, and improves previous results on general sequences where those queries are answered in O(log r) time. For larger r, we can still represent S in nH0(S) + o(n log r) bits and answer queries in O(log r / log log n) time. Another contribution of this paper is to show how to combine our compressed representation of integer sequences with an existing compression boosting technique to design compressed fulltext indexes that scale well with the size of the input alphabet Σ. Namely, we design a variant of the FMindex that indexes a string T [1, n] within nHk(T) + o(n) bits of storage, where Hk(T) is the kth order empirical entropy of T. This space bound holds simultaneously for all k ≤ α log Σ  n, constant 0 < α < 1, and Σ  = O(polylog(n)). This index counts the occurrences of an arbitrary pattern P [1, p] as a substring of T in O(p) time; it locates each pattern occurrence in O(log 1+ε n) time, for any constant 0 < ε < 1; and it reports a text substring of length ℓ in O(ℓ + log 1+ε n) time.
Practical implementation of rank and select queries
 In Poster Proceedings Volume of 4th Workshop on Efficient and Experimental Algorithms (WEA’05) (Greece
, 2005
"... Research on succinct data structures has made significant progress in recent years. An essential building block of many of those techniques is a data structure to perform rank and select operations over a bit array. The first operation tells how many bits are set up to some position, and the second ..."
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Cited by 38 (16 self)
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Research on succinct data structures has made significant progress in recent years. An essential building block of many of those techniques is a data structure to perform rank and select operations over a bit array. The first operation tells how many bits are set up to some position, and the second the position of the ith bit set. Albeit there exist constanttime solutions that require sublinear extra space, the practicality of those solutions against more naive ones has not been carefully studied. In this paper we show some results in this respect, which suggest that in many practical cases the simpler solutions are better in terms of time and extra space.
Rank and select revisited and extended
 Workshop on SpaceConscious Algorithms, University of
, 2006
"... The deep connection between the BurrowsWheeler transform (BWT) and the socalled rank and select data structures for symbol sequences is the basis of most successful approaches to compressed text indexing. Rank of a symbol at a given position equals the number of times the symbol appears in the corr ..."
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Cited by 33 (17 self)
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The deep connection between the BurrowsWheeler transform (BWT) and the socalled rank and select data structures for symbol sequences is the basis of most successful approaches to compressed text indexing. Rank of a symbol at a given position equals the number of times the symbol appears in the corresponding prefix of the sequence. Select is the inverse, retrieving the positions of the symbol occurrences. It has been shown that improvements to rank/select algorithms, in combination with the BWT, turn into improved compressed text indexes. This paper is devoted to alternative implementations and extensions of rank and select data structures. First, we show that one can use gap encoding techniques to obtain constant time rank and select queries in essentially the same space as what is achieved by the best current direct solution (and sometimes less). Second, we extend symbol rank and select to substring rank and select, giving several space/time tradeoffs for the problem. An application of these queries is in positionrestricted substring searching, where one can specify the range in the text where the search is restricted to, and only occurrences residing in that range are to be reported. In addition, arbitrary occurrences are reported in text position order. Several byproducts of our results display connections with searchable partial sums, Chazelle’s twodimensional data structures, and Grossi et al.’s wavelet trees.
Compact Suffix Array
, 2000
"... Suffix array is a data structure that can be used to index a large text le so that queries of its content can be answered quickly. Basically a suffix array is an array of all suffixes of the text in the lexicographic order. Whether or not a word occurs in the text can be answered in logarithmic time ..."
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Cited by 32 (10 self)
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Suffix array is a data structure that can be used to index a large text le so that queries of its content can be answered quickly. Basically a suffix array is an array of all suffixes of the text in the lexicographic order. Whether or not a word occurs in the text can be answered in logarithmic time by binary search over the suffix array. In this work we present a method to compress a suffix array such that the search time remains logarithmic. Our experiments show that in some cases a suffix array can be compressed by our method such that the total space requirement is about half of the original.
Statistical encoding of succinct data structures
 In Proc. 17th CPM, LNCS 4009
, 2006
"... Abstract. In recent work, Sadakane and Grossi [SODA 2006] introduced a scheme to represent any (k log σ + log log n)) bits sequence S = s1s2... sn, over an alphabet of size σ, using nHk(S) + O ( n log σ n of space, where Hk(S) is the kth order empirical entropy of S. The representation permits extr ..."
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Cited by 26 (11 self)
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Abstract. In recent work, Sadakane and Grossi [SODA 2006] introduced a scheme to represent any (k log σ + log log n)) bits sequence S = s1s2... sn, over an alphabet of size σ, using nHk(S) + O ( n log σ n of space, where Hk(S) is the kth order empirical entropy of S. The representation permits extracting any substring of size Θ(log σ n) in constant time, and thus it completely replaces S under the RAM model. This is extremely important because it permits converting any succinct data structure requiring o(S) = o(n log σ) bits in addition to S, into another requiring nHk(S) + o(n log σ) (overall) for any k = o(log σ n). They achieve this result by using ZivLempel compression, and conjecture that the result can in particular be useful to implement compressed fulltext indexes. In this paper we extend their result, by obtaining the same space and time complexities using a simpler scheme based on statistical encoding. We show that the scheme supports appending symbols in constant amortized time. In addition, we prove some results on the applicability of the scheme for fulltext selfindexing. 1
Compressed data structures: dictionaries and dataaware measures
 In Proc. 5th International Workshop on Experimental Algorithms (WEA
, 2006
"... Abstract. We propose measures for compressed data structures, in which space usage is measured in a dataaware manner. In particular, we consider the fundamental dictionary problem on set data, where the task is to construct a data structure to represent a set S of n items out of a universe U = {0,. ..."
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Cited by 23 (2 self)
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Abstract. We propose measures for compressed data structures, in which space usage is measured in a dataaware manner. In particular, we consider the fundamental dictionary problem on set data, where the task is to construct a data structure to represent a set S of n items out of a universe U = {0,..., u − 1} and support various queries on S. We use a wellknown dataaware measure for set data called gap to bound the space of our data structures. We describe a novel dictionary structure taking gap+O(n log(u/n) / log n)+O(n log log(u/n)) bits. Under the RAM model, our dictionary supports membership, rank, select, and predecessor queries in nearly optimal time, matching the time bound of Andersson and Thorup’s predecessor structure [AT00], while simultaneously improving upon their space usage. Our dictionary structure uses exactly gap bits in the leading term (i.e., the constant factor is 1) and answers queries in nearoptimal time. When seen from the worst case perspective, we present the first O(n log(u/n))bit dictionary structure which supports these queries in nearoptimal time under RAM model. We also build a dictionary which requires the same space and supports membership, select, and partial rank queries even more quickly in O(log log n) time. To the best of our knowledge, this is the first of a kind result which achieves dataaware space usage and retains nearoptimal time. 1
Advantages of backward searching — efficient secondary memory and distributed implementation of compressed suffix arrays
, 2004
"... Abstract. One of the most relevant succinct suffix array proposals in the literature is the Compressed Suffix Array (CSA) of Sadakane [ISAAC 2000]. The CSA needs n(H0 + O(log log σ)) bits of space, where n is the text size, σ is the alphabet size, and H0 the zeroorder entropy of the text. The numbe ..."
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Cited by 17 (11 self)
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Abstract. One of the most relevant succinct suffix array proposals in the literature is the Compressed Suffix Array (CSA) of Sadakane [ISAAC 2000]. The CSA needs n(H0 + O(log log σ)) bits of space, where n is the text size, σ is the alphabet size, and H0 the zeroorder entropy of the text. The number of occurrences of a pattern of length m can be computed in O(m log n) time. Most notably, the CSA does not need the text separately available to operate. The CSA simulates a binary search over the suffix array, where the query is compared against text substrings. These are extracted from the same CSA by following irregular access patterns over the structure. Sadakane [SODA 2002] has proposed using backward searching on the CSA in similar fashion as the FMindex of Ferragina and Manzini [FOCS 2000]. He has shown that the CSA can be searched in O(m) time whenever σ = O(polylog(n)). In this paper we consider some other consequences of backward searching applied to CSA. The most remarkable one is that we do not need, unlike all previous proposals, any complicated sublinear structures based on the fourRussians technique (such as constant time rank and select queries on bit arrays). We show that sampling and compression are enough to achieve O(m log n) query time using less space than the original structure. It is also possible to trade structure space for search time. Furthermore, the regular access pattern of backward searching permits an efficient secondary memory implementation, so that the search can be done with O(m log B n) disk accesses, being B the disk block size. Finally, it permits a distributed implementation with optimal speedup and negligible communication effort.