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19
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 193 (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
, 2005
"... 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. ..."
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Cited by 188 (17 self)
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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
Compressed suffix trees with full functionality
 Theory of Computing Systems
"... We introduce new data structures for compressed suffix trees whose size are linear in the text size. The size is measured in bits; thus they occupy only O(n log A) bits for a text of length n on an alphabet A. This is a remarkable improvement on current suffix trees which require O(n log n) bits. ..."
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Cited by 53 (5 self)
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We introduce new data structures for compressed suffix trees whose size are linear in the text size. The size is measured in bits; thus they occupy only O(n log A) bits for a text of length n on an alphabet A. This is a remarkable improvement on current suffix trees which require O(n log n) bits. Though some components of suffix trees have been compressed, there is no linearsize data structure for suffix trees with full functionality such as computing suffix links, stringdepths and lowest common ancestors. The data structure proposed in this paper is the first one that has linear size and supports all operations efficiently. Any algorithm running on a suffix tree can also be executed on our compressed suffix trees with a slight slowdown of a factor of polylog(n). 1
Succinct Representations of lcp Information and Improvements in the Compressed Suffix Arrays
, 2002
"... We introduce two succinct data structures to solve various string problems. One is for storing the information of lcp, the longest common prefix, between suffixes in the suffix array, and the other is an improvement in the compressed suffix array which supports linear time counting queries for any p ..."
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Cited by 50 (6 self)
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We introduce two succinct data structures to solve various string problems. One is for storing the information of lcp, the longest common prefix, between suffixes in the suffix array, and the other is an improvement in the compressed suffix array which supports linear time counting queries for any pattern. The former occupies only 2n + o(n) bits for a text of length n for computing lcp between adjacent suffixes in lexicographic order in constant time, and 6n + o(n) bits between any two suffixes. No data structure in the literature attained linear size. The latter has size proportional to the text size and it is applicable to texts on any alphabet Σ such that Σ = log^O(1) n. These spaceeconomical data structures are useful in processing huge amounts of text data.
Membership in Constant Time and Minimum Space
 Lecture Notes in Computer Science
, 1994
"... . We investigate the problem of storing a subset of the elements of a boundeduniverse so that searches canbe performed in constant time and the space used is within a constant factor of the minimum required. Initially we focus on the static version of this problem and conclude with an enhancement th ..."
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Cited by 18 (5 self)
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. We investigate the problem of storing a subset of the elements of a boundeduniverse so that searches canbe performed in constant time and the space used is within a constant factor of the minimum required. Initially we focus on the static version of this problem and conclude with an enhancement that permits insertions and deletions. 1 Introduction Given a universal set M = f0; : : : ; M \Gamma 1g and any subset N = fe 1 ; : : : ; e N g the membership problem is to determine whether given query element in M is an element of N . There are two standard approaches to solve this problem: to list all elements of N (e.g. in a hash table) or to list all the answers (e.g. a bit map of size M ). When N is small the former approach comes close to the information theoretic lower bound on the number of bits needed to represent an arbitrary subset of the given size (i.e. a function of both N and M , l lg \Gamma M N \Delta m ). Similarly, when N is large (say ffM ) the later approach is near...
Theory and Practise of Monotone Minimal Perfect Hashing
"... Minimal perfect hash functions have been shown to be useful to compress data in several data management tasks. In particular, orderpreserving minimal perfect hash functions [12] have been used to retrieve the position of a key in a given list of keys: however, the ability to preserve any given orde ..."
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Cited by 13 (6 self)
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Minimal perfect hash functions have been shown to be useful to compress data in several data management tasks. In particular, orderpreserving minimal perfect hash functions [12] have been used to retrieve the position of a key in a given list of keys: however, the ability to preserve any given order leads to an unavoidable �(n log n) lower bound on the number of bits required to store the function. Recently, it was observed [1] that very frequently the keys to be hashed are sorted in their intrinsic (i.e., lexicographical) order. This is typically the case of dictionaries of search engines, list of URLs of web graphs, etc. We refer to this restricted version of the problem as monotone minimal perfect hashing. We analyse experimentally the data structures proposed in [1], and along our way we propose some new methods that, albeit asymptotically equivalent or worse, perform very well in practise, and provide a balance between access speed, ease of construction, and space usage. 1
MORE HASTE, LESS WASTE: LOWERING THE REDUNDANCY IN FULLY INDEXABLE DICTIONARIES
, 2009
"... We consider the problem of representing, in a compressed format, a bitvector S of m bits with n 1s, supporting the following operations, where b ∈ {0,1}: • rankb(S, i) returns the number of occurrences of bit b in the prefix S [1..i]; • selectb(S, i) returns the position of the ith occurrence of bi ..."
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Cited by 5 (0 self)
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We consider the problem of representing, in a compressed format, a bitvector S of m bits with n 1s, supporting the following operations, where b ∈ {0,1}: • rankb(S, i) returns the number of occurrences of bit b in the prefix S [1..i]; • selectb(S, i) returns the position of the ith occurrence of bit b in S. Such a data structure is called fully indexable dictionary (fid) [Raman, Raman, and Rao, 2007], and is at least as powerful as predecessor data structures. Viewing S as a set X = {x1, x2,..., xn} of n distinct integers drawn from a universe [m] = {1,..., m}, the predecessor of integer y ∈ [m] in X is given by select1(S,rank1(S, y − 1)). fids have many applications in succinct and compressed data structures, as they are often involved in the construction of succinct representation for a variety of abstract data types. Our focus is on spaceefficient fids on the ram model with word size Θ(lg m) and constant time for all operations, so that the time cost is independent of the input size. Given the bitstring S to be encoded, having length m and containing n ones, the minimal amount of information that needs to be stored is B(n, m) = ⌈log ` ´ m ⌉. The n state of the art in building a fid for S is given in [Pǎtra¸scu, 2008] using B(m, n) + O(m/((log m/t) t)) + O(m 3/4) bits, to support the operations in O(t) time. Here, we propose a parametric data structure exhibiting a time/space tradeoff such that, for any real constants 0 < δ ≤ 1/2, 0 < ε ≤ 1, and integer s> 0, it uses
A Large Time Aware Web Graph
"... We describe the techniques developed to gather and distribute in a highly compressed, yet accessible, form a series of twelve snapshot of the.uk web domain. Ad hoc compression techniques made it possible to store the twelve snapshots using just 1.9 bits per link, with constanttime access to tempora ..."
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Cited by 5 (0 self)
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We describe the techniques developed to gather and distribute in a highly compressed, yet accessible, form a series of twelve snapshot of the.uk web domain. Ad hoc compression techniques made it possible to store the twelve snapshots using just 1.9 bits per link, with constanttime access to temporal information. Our collection makes it possible to study the temporal evolution linkbased scores (e.g., PageRank), the growth of online communities, and in general timedependent phenomena related to the link structure.
Efficient approximate dictionary lookup over small alphabets
, 2005
"... Given a dictionary W consisting of n binary strings of length m each, a dquery asks if there exists a string in W within Hamming distance d of a given binary query string q. The problem was posed by Minsky and Papert in 1969 [10] as a challenge to data structure design. Efficient solutions have bee ..."
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Cited by 3 (1 self)
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Given a dictionary W consisting of n binary strings of length m each, a dquery asks if there exists a string in W within Hamming distance d of a given binary query string q. The problem was posed by Minsky and Papert in 1969 [10] as a challenge to data structure design. Efficient solutions have been developed only for the special case when d = 1 (the 1query problem). We assume the standard RAM model of computation, and consider the case of the problem when alphabet size is arbitrary but finite, and d is small. We preprocess the dictionary, and construct an edgelabelled tree with bounded branching factor, and height. We present an algorithm to answer dictionary lookup within given distance d of a given query string q. The algorithm is efficient when the alphabet size is small, or the dictionary is sparse. In particular, for the dquery problem the algorithm takes time O(m(log 4/3 n − 1) d (log 2 n) d+1). This is an improvement over previously known algorithms for the dquery problem when d> 1. We also generalize the results for the case of the problem when edit distances are used. The algorithm can be modified such that it allows for words of different lengths as well as different lengths of query strings. 1
DACs: Bringing Direct Access to VariableLength Codes
, 2012
"... We present a new variablelength encoding scheme for sequences of integers, Directly Addressable Codes (DACs), which enables direct access to any element of the encoded sequence without the need of any sampling method. Our proposal is a kind of implicit data structure that introduces synchronism in ..."
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Cited by 3 (1 self)
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We present a new variablelength encoding scheme for sequences of integers, Directly Addressable Codes (DACs), which enables direct access to any element of the encoded sequence without the need of any sampling method. Our proposal is a kind of implicit data structure that introduces synchronism in the encoded sequence without using asymptotically any extra space. We show some experiments demonstrating that the technique is not only simple, but also competitive in time and space with existing solutions in several applications, such as the representation of LCP arrays or highorder entropycompressed sequences.