Improving on-line construction of two-dimensional suffix trees for square matrices

The two-dimensional (2-D) suffix tree of an n×n square matrix A is a compacted trie that represents all square submatrices of A. We consider constructing 2-D suffix trees on-line, which means, instead of giving the whole matrix A in advance, A is separated and each part of A is given at different time as algorithms proceed. In general, developing an on-line algorithm is more difficult than developing an off-line algorithm. Moreover, the smaller the input grain size is, the harder it is to develop an on-line algorithm. In the case of 2-D suffix tree construction, dealing with a character at a time is harder than dealing with a row or a column at a time.In this paper we propose a randomized linear-time algorithm for constructing 2-D suffix trees on-line. This algorithm is superior to previous algorithms in two ways: (1) This is the first linear-time algorithm for constructing 2-D suffix trees on-line. Although there have been some linear-time algorithms for off-line construction, there were no linear-time algorithms for on-line construction. (2) We deal with the most fine-grain on-line case, i.e., our algorithm can construct a 2-D suffix tree even though only one character of A is given at a time, while previous on-line algorithms require at least a row and/or a column at a time.     

On-line construction of suffix trees

An on-line algorithm is presented for constructing the suffix tree for a given string in time linear in the length of the string. The new algorithm has the desirable property of processing the string symbol by symbol from left to right. It always has the suffix tree for the scanned part of the string ready. The method is developed as a linear-time version of a very simple algorithm for (quadratic size) suffixtries. Regardless of its quadratic worst case this latter algorithm can be a good practical method when the string is not too long. Another variation of this method is shown to give, in a natural way, the well-known algorithms for constructing suffix automata (DAWGs).This research was supported by the Academy of Finland and by the Alexander von Humboldt Foundation (Germany).     

Randomized splay trees: Theoretical and experimental results

Splay trees are self-organizing binary search trees that were introduced by Sleator and Tarjan [J. ACM 32 (1985) 652-686]. In this paper we present a randomized variant of these trees. The new algorithm for reorganizing the tree is both simple and easy to implement. We prove that our randomized splaying scheme has the same asymptotic performance as the original deterministic scheme but improves constants in the expected running time. This is interesting in practice because the search time in splay trees is typically higher than the search time in skip lists and AVL-trees. We present a detailed experimental study of our algorithm. On request sequences generated by fixed probability distributions, we can achieve improvements of up to 25% over deterministic splaying. On request sequences that exhibit high locality of reference, the improvements are minor.     

k-Restricted rotation distance between binary trees

The restricted rotation distancedR(S,T) between two binary trees S, T of n vertices is the minimum number of rotations to transform S into T, where rotations take place at the root of S, or at the right child of the root. A sharp upper bound dR(S,T)?4n−8 is known, based on group theory [S. Cleary, J. Taback, Bounding restricted rotation distance, Information Processing Letters 88 (5) (2003) 251-256]. We refine this bound to a sharp dR(S,T)?4n−8−ρS−ρT, where ρS and ρT are the numbers of vertices in the rightmost vertex chains of the two trees, using an elementary transformation algorithm. We then generalize the concept to k-restricted rotation, by allowing rotations to take place at all the vertices of the highest k levels of the tree, and study the new distance for k=2. The case k?3 is essentially open.     

Using spine decompositions to efficiently solve the length-constrained heaviest path problem for trees

The length-constrained heaviest path (LCHP) in a weighted tree T, where each edge is assigned a weight and a length, is the path P in T with maximum total path weight and total path length bounded by a given value B. This paper presents an O(nlogn) time LCHP algorithm which utilizes a data structure constructed from the spine decomposition of the input tree. This is an improvement over the existing algorithm by Wu et al. (1999), which runs in O(nlog²n) time. Our method also improves on a previous O(nlogn) time algorithm by Kim (2005) for the special case of finding a longest nonnegative path in a constant degree tree in that we can handle trees of arbitrary degree within the same time bounds.     

Faster semi-external suffix sorting

Suffix array (SA) construction is a time-and-memory bottleneck in many string processing applications. In this paper we improve the runtime of a small-space — semi-external — SA construction algorithm by Kärkkäinen (TCS, 2007) [5]. We achieve a speedup in practice of 2–4 times, without increasing memory usage. Our main contribution is a way to implement the “pointer copying” heuristic, used in less space-efficient SA construction algorithms, in a memory-efficient way.     

Trading uninitialized space for time

The design of efficient graph algorithms usually precludes the test of edge existence, because an efficient support of that operation already requires time for the initialization of an adjacency-matrix representation. We describe an alternative representation of static directed graphs taking Θ(n+m) initialization time and using Θ(n²) space, which supports the efficient implementation of all usual operations on static graphs. The sparse graph representation allows the design of efficient graph algorithms using both iteration over all vertices adjacent with a given vertex and edge-existence operations, although at the expense of additional (uninitialized) space which may, nevertheless, be used for other purposes. To the best of our knowledge, the representation leads to the first graph algorithms with the disconcerting property that the time complexity is better than the space complexity.     

Construction of Aho Corasick automaton in linear time for integer alphabets

We present a new simple algorithm that constructs an Aho Corasick automaton for a set of patterns, P, of total length n, in O(n) time and space for integer alphabets. Processing a text of size m over an alphabet Σ with the automaton costs O(mlog|Σ|+k), where there are k occurrences of patterns in the text.A new, efficient implementation of nodes in the Aho Corasick automaton is introduced, which works for suffix trees as well.     

Time-space trade-offs for compressed suffix arrays

Given a binary string of length n, we give a representation of its suffix array that takes O(nt(lgn)^1/t) bits of space such that given i,1?i?n, the ith entry in the suffix array of the string can be retrieved in O(t) time, for any parameter 1?t?lglgn. For t=lglgn, this gives a compressed suffix array representation of Grossi and Vitter [Proc. Symp. on Theory Comput., 2000, pp. 397-406]. For t=O(1/ε), this gives the best known (in terms of space) compressed suffix array representation with constant query time. From this representation one can construct a suffix tree structure for a text of length n, that uses o(nlgn) bits of space which can be used to find all the k occurrences of a given pattern of length m in O(m/lgn+k) time. No such structure was known earlier.     

Uniform metrical task systems with a limited number of states

We give a randomized algorithm (the “Wedge Algorithm”) of competitiveness for any metrical task system on a uniform space of k points, for any k?2, where , the kth harmonic number. This algorithm has better competitiveness than the Irani-Seiden algorithm if k is smaller than 10⁸. The algorithm is better by a factor of 2 if k<47.     

Efficient unbalanced merge-sort

Sorting algorithms based on successive merging of ordered subsequences are widely used, due to their efficiency and to their intrinsically parallelizable structure. Among them, the merge-sort algorithm emerges indisputably as the most prominent method. In this paper we present a variant of merge-sort that proceeds through arbitrary merges between pairs of quasi-ordered subsequences, no matter which their size may be. We provide a detailed analysis, showing that a set of n elements can be sorted by performing at most n⌊logn⌋ key comparisons. Our method has the same optimal asymptotic time and space complexity as compared to previous known unbalanced merge-sort algorithms, but experimental results show that it behaves significantly better in practice.     

The interval-merging problem

A closed interval is an ordered pair of real numbers [x, y], with x ? y. The interval [x, y] represents the set {i ∈ R∣x ? i ? y}. Given a set of closed intervals I={[a₁,b₁],[a₂,b₂],…,[a_k,b_k]}, the Interval-Merging Problem is to find a minimum-cardinality set of intervals M(I)={[x₁,y₁],[x₂,y₂],…,[x_j,y_j]}, j ? k, such that the real numbers represented by equal those represented by . In this paper, we show the problem can be solved in O(d log d) sequential time, and in O(log d) parallel time using O(d) processors on an EREW PRAM, where d is the number of the endpoints of I. Moreover, if the input is given as a set of sorted endpoints, then the problem can be solved in O(d) sequential time, and in O(log d) parallel time using O(d/log d) processors on an EREW PRAM.     

Scalable Bloom Filters

Bloom filters provide space-efficient storage of sets at the cost of a probability of false positives on membership queries. The size of the filter must be defined a priori based on the number of elements to store and the desired false positive probability, being impossible to store extra elements without increasing the false positive probability. This leads typically to a conservative assumption regarding maximum set size, possibly by orders of magnitude, and a consequent space waste. This paper proposes Scalable Bloom Filters, a variant of Bloom filters that can adapt dynamically to the number of elements stored, while assuring a maximum false positive probability.     

On the false-positive rate of Bloom filters

Bloom filters are a randomized data structure for membership queries dating back to 1970. Bloom filters sometimes give erroneous answers to queries, called false positives. Bloom analyzed the probability of such erroneous answers, called the false-positive rate, and Bloom's analysis has appeared in many publications throughout the years. We show that Bloom's analysis is incorrect and give a correct analysis.     

On the existence and construction of non-extreme (a,b)-trees

In amortized analysis of data structures, it is standard to assume that initially the structure is empty. Usually, results cannot be established otherwise. In this paper, we investigate the possibilities of establishing such results for initially non-empty multi-way trees.     

Constructing independent spanning trees for locally twisted cubes

The independent spanning trees (ISTs) problem attempts to construct a set of pairwise independent spanning trees and it has numerous applications in networks such as data broadcasting, scattering and reliable communication protocols. The well-known ISTs conjecture, Vertex/Edge Conjecture, states that any n-connected/n-edge-connected graph has n vertex-ISTs/edge-ISTs rooted at an arbitrary vertex r. It has been shown that the Vertex Conjecture implies the Edge Conjecture. In this paper, we consider the independent spanning trees problem on the n-dimensional locally twisted cube LTQ_n. The very recent algorithm proposed by Hsieh and Tu (2009) [12] is designed to construct n edge-ISTs rooted at vertex 0 for LTQ_n. However, we find out that LTQ_n is not vertex-transitive when n≥4; therefore Hsieh and Tu’s result does not solve the Edge Conjecture for LTQ_n. In this paper, we propose an algorithm for constructing n vertex-ISTs for LTQ_n; consequently, we confirm the Vertex Conjecture (and hence also the Edge Conjecture) for LTQ_n.     

Canonical density control

The Sparse Table is a data structure for controlling density in an array which was first proposed in 1981 and has recently reappeared as a component of cache-oblivious data structures. All existing variants of the Sparse Table divide the array into blocks that have a calibrator tree above them. We show that the same amortized complexity can be achieved without this auxiliary structure, obtaining a canonical data structure that can be updated by conceptually simpler algorithms.     

Covering sharing trees: a compact data structure for parameterized verification

The control state reachability problem is decidable for well-structured infinite-state systems like (Lossy) Petri Nets, Vector Addition Systems, and broadcast protocols. An abstract algorithm that solves the problem is the backward reachability algorithm of [1, 21 ]. The algorithm computes the closure of the predecessor operator with respect to a given upward-closed set of target states. When applied to this class of verification problems, symbolic model checkers based on constraints like [7, 26 ] suffer from the state explosion problem.In order to tackle this problem, in [13] we introduced a new data structure, called covering sharing trees, to represent in a compact way collections of infinite sets of system configurations. In this paper, we will study the theoretical complexity of the operations over covering sharing trees needed in symbolic model checking. We will also discuss several optimizations that can be used when dealing with Petri Nets. Among them, in [14] we introduced a new heuristic rule based on structural properties of Petri Nets that can be used to efficiently prune the search during symbolic backward exploration. The combination of these techniques allowed us to turn the abstract algorithm of [1, 21 ] into a practical method. We have evaluated the method on several finite-state and infinite-state examples taken from the literature [2, 18 , 20 , 30 ]. In this paper, we will compare the results we obtained in our experiments with those obtained using other finite and infinite-state verification tools.