Results 1  10
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12
Cuckoo hashing
 Journal of Algorithms
, 2001
"... We present a simple dictionary with worst case constant lookup time, equaling the theoretical performance of the classic dynamic perfect hashing scheme of Dietzfelbinger et al. (Dynamic perfect hashing: Upper and lower bounds. SIAM J. Comput., 23(4):738–761, 1994). The space usage is similar to that ..."
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Cited by 124 (6 self)
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We present a simple dictionary with worst case constant lookup time, equaling the theoretical performance of the classic dynamic perfect hashing scheme of Dietzfelbinger et al. (Dynamic perfect hashing: Upper and lower bounds. SIAM J. Comput., 23(4):738–761, 1994). The space usage is similar to that of binary search trees, i.e., three words per key on average. Besides being conceptually much simpler than previous dynamic dictionaries with worst case constant lookup time, our data structure is interesting in that it does not use perfect hashing, but rather a variant of open addressing where keys can be moved back in their probe sequences. An implementation inspired by our algorithm, but using weaker hash functions, is found to be quite practical. It is competitive with the best known dictionaries having an average case (but no nontrivial worst case) guarantee. Key Words: data structures, dictionaries, information retrieval, searching, hashing, experiments * Partially supported by the Future and Emerging Technologies programme of the EU
Suffix Trees on Words
, 1995
"... We present an intrinsic generalization on the suffix tree, designed to index a string of length n which has a natural partitioning into m multicharacter substrings or words. The word suffix tree represents only the m suffixes that start at word boundaries. These boundaries are determined by delimit ..."
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Cited by 31 (2 self)
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We present an intrinsic generalization on the suffix tree, designed to index a string of length n which has a natural partitioning into m multicharacter substrings or words. The word suffix tree represents only the m suffixes that start at word boundaries. These boundaries are determined by delimiters, whose definition depends on the application. Since traditional suffix tree construction algorithms rely heavily on the fact that all suffixes are inserted, construction of a word suffix tree is nontrivial, in particular when only O(m) construction space is allowed. We solve this problem, presenting an algorithm with O(n) expected running time. In general, construction cost is \Omega(n) due to the need of scanning the entire input. In applications that require strict node ordering, an additional cost of sorting O(m') characters arises, where m' is the number of distinct words. This is a significant improvement over previous solutions. In some cases, when the alphabet is small, we may assume that the n characters in the input string occupy o(n) machine words. We show that this can allow a word suffix tree to be built in sublinear time.
Low Redundancy in Dictionaries with O(1) Worst Case Lookup Time
 IN PROC. 26TH INTERNATIONAL COLLOQUIUM ON AUTOMATA, LANGUAGES AND PROGRAMMING (ICALP
, 1998
"... A static dictionary is a data structure for storing subsets of a finite universe U , so that membership queries can be answered efficiently. We study this problem in a unit cost RAM model with word size ze jU j), and show that for nelement subsets, constant worst case query time can be obtain ..."
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Cited by 18 (0 self)
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A static dictionary is a data structure for storing subsets of a finite universe U , so that membership queries can be answered efficiently. We study this problem in a unit cost RAM model with word size ze jU j), and show that for nelement subsets, constant worst case query time can be obtained using B +O(log log jU j) + o(n) bits of storage, where B = dlog jU j e is the minimum number of bits needed to represent all such subsets. The solution for dense subsets uses B + O( jU j log log jU j log jU j ) bits of storage, and supports constant time rank queries. In a dynamic setting, allowing insertions and deletions, our techniques give an O(B) bit space usage.
Ordering Constraints over Feature Trees
, 1999
"... Feature trees are the formal basis for algorithms manipulating record like structures in constraint programming, computational linguistics and in concrete applications like software configuration management. Feature trees model records, and constraints over feature trees yield extensible and modular ..."
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Cited by 14 (5 self)
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Feature trees are the formal basis for algorithms manipulating record like structures in constraint programming, computational linguistics and in concrete applications like software configuration management. Feature trees model records, and constraints over feature trees yield extensible and modular record descriptions. We introduce the constraint system FT of ordering constraints interpreted over feature trees. Under the view that feature trees represent symbolic information, the relation corresponds to the information ordering ("carries less information than"). We present two algorithms in cubic time, one for the satisfiability problem and one for the entailment problem of FT . We show that FT has the independence property. We are thus able to handle negative conjuncts via entailment and obtain a cubic algorithm that decides the satisfiability of conjunctions of positive and negated ordering constraints over feature trees. Furthermore, we reduce the satisfiability problem of Dorre's weak subsumption constraints to the satisfiability problem of FT and improve the complexity bound for solving weak subsumption constraints from O(n^5) to O(n³).
Perfect hashing for network applications
 in IEEE Symposium on Information Theory
, 2006
"... Abstract — Hash tables are a fundamental data structure in many network applications, including route lookups, packet classification and monitoring. Often a part of the data path, they need to operate at wirespeed. However, several associative memory accesses are needed to resolve collisions, makin ..."
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Cited by 10 (1 self)
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Abstract — Hash tables are a fundamental data structure in many network applications, including route lookups, packet classification and monitoring. Often a part of the data path, they need to operate at wirespeed. However, several associative memory accesses are needed to resolve collisions, making them slower than required. This motivates us to consider minimal perfect hashing schemes, which reduce the number of memory accesses to just 1 and are also spaceefficient. Existing perfect hashing algorithms are not tailored for network applications because they take too long to construct and are hard to implement in hardware. This paper introduces a hardwarefriendly scheme for minimal perfect hashing, with space requirement approaching 3.7 times the information theoretic lower bound. Our construction is several orders faster than existing perfect hashing schemes. Instead of using the traditional mappingpartitioningsearching methodology, our scheme employs a Bloom filter, which is known for its simplicity and speed. We extend our scheme to the dynamic setting, thus handling insertions and deletions. I.
Faster Deterministic Dictionaries
 In 11 th Annual ACM Symposium on Discrete Algorithms (SODA
, 1999
"... We consider static dictionaries over the universe U = on a unitcost RAM with word size w. Construction of a static dictionary with linear space consumption and constant lookup time can be done in linear expected time by a randomized algorithm. In contrast, the best previous deterministic a ..."
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Cited by 9 (5 self)
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We consider static dictionaries over the universe U = on a unitcost RAM with word size w. Construction of a static dictionary with linear space consumption and constant lookup time can be done in linear expected time by a randomized algorithm. In contrast, the best previous deterministic algorithm for constructing such a dictionary with n elements runs in time O(n ) for # > 0. This paper narrows the gap between deterministic and randomized algorithms exponentially, from the factor of to an O(log n) factor. The algorithm is weakly nonuniform, i.e. requires certain precomputed constants dependent on w. A byproduct of the result is a lookup time vs insertion time tradeo# for dynamic dictionaries, which is optimal for a certain class of deterministic hashing schemes.
Exploiting Functional Dependence in Query Optimization
, 2000
"... I authorize the University of Waterloo to lend this thesis to other institutions or individuals for the purpose of scholarly research. I further authorize the University of Waterloo to reproduce this thesis by photocopying or by other means, in total or in part, at the request of other institutions ..."
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Cited by 2 (0 self)
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I authorize the University of Waterloo to lend this thesis to other institutions or individuals for the purpose of scholarly research. I further authorize the University of Waterloo to reproduce this thesis by photocopying or by other means, in total or in part, at the request of other institutions or individuals for the purpose of scholarly research. iii The University of Waterloo requires the signatures of all persons using or photocopying this thesis. Please sign below, and give address and date. Functional dependency analysis can be applied to various problems in query optimization: selectivity estimation, estimation of (intermediate) result sizes, order optimization (in particular sort avoidance), cost estimation, and various problems in the area of semantic query optimization. Dependency analysis in an ansi sql relational model, however, is made complex due to the existence of null values, threevalued logic, outer joins, and duplicate rows. In this thesis we define the notions of strict and lax functional dependencies, strict and lax equivalence constraints, and null constraints, which capture both a
A New Tradeoff for Deterministic Dictionaries
, 2000
"... . We consider dictionaries over the universe U = f0; 1g w on a unitcost RAM with word size w and a standard instruction set. We present a linear space deterministic dictionary with membership queries in time (log log n) O(1) and updates in time (log n) O(1) , where n is the size of the se ..."
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Cited by 1 (0 self)
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. We consider dictionaries over the universe U = f0; 1g w on a unitcost RAM with word size w and a standard instruction set. We present a linear space deterministic dictionary with membership queries in time (log log n) O(1) and updates in time (log n) O(1) , where n is the size of the set stored. This is the rst such data structure to simultaneously achieve query time (log n) o(1) and update time O(2 (log n) c ) for a constant c < 1. 1 Introduction Among the most fundamental data structures is the dictionary. A dictionary stores a subset S of a universe U , oering membership queries of the form \x 2 S?". The result of a membership query is either 'no' or a piece of satellite data associated with x. Updates of the set are supported via insertion and deletion of single elements. Several performance measures are of interest for dictionaries: The amount of space used, the time needed to answer queries, and the time needed to perform updates. The most ecient dictionar...