后缀树构造算法理论分析

后缀树的结构简单,但可以在线性的时间里解决许多复杂的问题,被大量的使用在字符串及树的模式匹配中.     

维吾尔文后缀树构造算法的设计与实现

为用后缀树聚类算法对维吾尔文网页进行聚类,通过分析可扩展后缀树和维吾尔文的特点设计了维吾尔文后缀树构造算法。实验结果证明该方法能够在线性的时间范围内构造维吾尔文后缀树,并用它来对维吾尔文网页进行聚类。     

后缀树的设计与构造

后缀树是处理字符串的一个优秀算法。利用图像化设计可使后缀树更加清晰。按照递推的思路,建立前i个字符对应的后缀树,通过插入第i+1个字符的方式,建立前i+1个字符对应的后缀树。由于字符串的任意子串都可以表示为某个后缀的前缀,因此可以设定当前节点为根节点。父节点取子节点中贡献最大的节点,同时,记录其对应的字符串。     

一种适合于GPU计算的并行后缀数组构造算法

后缀数组广泛应用于序列分析、字符串匹配和文本压缩,近年来,有关后缀数组构造和应用算法的不断探索构成了计算机科学中一个非常活跃的研究领域.在对现有串行算法进行了分析和对比之后,提出了一种新的、简洁的适合于GPU计算的并行后缀数组倍增构造算法,以排序方法替代传统的分组策略,不但能独立完成后缀数组的并行构造,还可与现存的串行倍增算法结合使用,以达到最高的执行效率.实验结果表明该算法在解决实际应用问题时,具有易于实现、执行速度快和可扩展性强等优点,尤其在处理小字符集的数据时效率更高.     

构建系统发生树后缀表示的蚁群算法

提出一种基于后缀表示的构建系统发生树的蚁群算法(SR-PTC),该算法用蚂蚁访问物种集合以形成一个对应最优系统发生树的后缀表示序列。为构成一个合法的系统发生树的后缀表示,蚂蚁对内部结点的选择要受到限制,分别为叶结点和内部结点设置两个不同的选择概率,并用赌轮盘选择方法来决定两种结点的选择。另外,在信息素更新时,加入当前树的评价值来影响蚂蚁的运动方向。实验结果表明,此方法能得到较为准确的拓扑结构,在物种数目较小时可以较快地得到结果。     

DNA序列中基于适应性后缀树的重复体识别算法

现有的在DNA序列中识别重复体的算法多数是基于比对的,对识别速度和吞吐量有很大的限制.针对这个问题文中根据一个平衡重复体的长度和频率的定义,提出了一种基于Ukkonen后缀树的快速识别重复体的RepSeeker算法.算法采用最低限制频率,最大程度地扩展了重复体的长度,同时为了进一步地提高RepSeeker算法的效率,对Ukkonen的后缀树构造算法进行了适应性改进,在构造时加入RepSeeker算法所需的结点信息并将叶子结点和分支结点加以区分,从而使得RepSeeker算法能通过直接读取结点信息来求得子串频率和子串位置.这种改进较大地提高了RepSeeker算法的性能,而且空间开销不大.实验中使用了NCBI中的9条典型DNA序列作为测试数据,并对后缀树改进前后的重复体识别算法做了比较分析.结果表明,RepSeeker在没有损失精度的情况下缩短了算法的运行时间.实验结果与理论上的分析一致.     

入侵检测中基于后缀树的多模式匹配算法

针对模式匹配算法已经成为误用入侵检测系统性能瓶颈的现状,提出了一种新的基于后缀树的多模式匹配算法FSM(Fast String Matching Algorithm).该算法构建了一个后缀自动机,匹配中应用了好后缀启发机制进行启发跳跃.将改算法在Snort2.4.3中实现,实验结果表明,在耗费一定空间的基础上,Snort的时间性能有了较大提高.     

基于并行B+-树的并行Join算法的设计、分析与实现

Ｂ＾＋－树是一种有效的数据库存储结构，被普遍应用于各种关系数据库系统。把Ｂ＾＋－树并行化，使之用于并行数据库系统显然是一项很有意义的重要工作。本文研究了适用于并行数据库的并行Ｂ＾＋－树存储结构，提出两类基于并行Ｂ＾＋－树工并行Ｊｏｉｎ算法。理论和实验结果表明，这些算法效率高基其它并行Ｊｏｉｎ算法。     

基于HRR划分的并行RDBn树Join算法

文章首先介绍了PDBMS采用的Hash-Round-Robin(HRR)数据划分方法以及基于该划分方法的并行RDBn树,最后着重、详细地给出了基于该树的并行Join算法,分析了该算法的效率。     

博弈树并行搜索算法

本文分析了博弈树并行搜索算法中的主变量分裂算法的同步开锁问题。     

Practical methods for constructing suffix trees

Sequence datasets are ubiquitous in modern life-science applications, and querying sequences is a common and critical operation in many of these applications. The suffix tree is a versatile data structure that can be used to evaluate a wide variety of queries on sequence datasets, including evaluating exact and approximate string matches, and finding repeat patterns. However, methods for constructing suffix trees are often very time-consuming, especially for suffix trees that are large and do not fit in the available main memory. Even when the suffix tree fits in memory, it turns out that the processor cache behavior of theoretically optimal suffix tree construction methods is poor, resulting in poor performance. Currently, there are a large number of algorithms for constructing suffix trees, but the practical tradeoffs in using these algorithms for different scenarios are not well characterized. In this paper, we explore suffix tree construction algorithms over a wide spectrum of data sources and sizes. First, we show that on modern processors, a cache-efficient algorithm with O(n²) worst-case complexity outperforms popular linear time algorithms like Ukkonen and McCreight, even for in-memory construction. For larger datasets, the disk I/O requirement quickly becomes the bottleneck in each algorithm's performance. To address this problem, we describe two approaches. First, we present a buffer management strategy for the O(n²) algorithm. The resulting new algorithm, which we call “Top Down Disk-based” (TDD), scales to sizes much larger than have been previously described in literature. This approach far outperforms the best known disk-based construction methods. Second, we present a new disk-based suffix tree construction algorithm that is based on a sort-merge paradigm, and show that for constructing very large suffix trees with very little resources, this algorithm is more efficient than TDD.     

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.     

A Linear Time Lower Bound on McCreight and General Updating Algorithms for Suffix Trees

Suffix trees are the fundamental data structure of combinatorial pattern matching on words. Suffix trees have been used in order to give optimal solutions to a great variety of problems on static words, but for practical situations, such as in a text editor, where the incremental changes of the text make dynamic updating of the corresponding suffix trees necessary, this data structure alone has not been used with success. We prove that, for dynamic modifications of order O(1) of words of length n, any suffix tree updating algorithm, such as the ones proposed by McCreight, requires O(n) worst-case running time, as for the full reconstruction of the suffix tree. Consequently, we argue that this data structure alone is not appropriate for the solution of combinatorial problems on words that change dynamically.     

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).     

后缀数组创建算法的分析和比较

后缀数组构建算法的时间和空间开销是它在实际应用中的瓶颈。该文介绍了两种较好的构建算法,对它们的性能作了评估和分析,指出了各自的适用范围,给出并比较了两种算法在不同情况下的实验结果。     

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.     

基于后缀树的带有通配符的模式匹配研究

由于在生物序列分析、文本索引、网络入侵检测等领域的应用需求,带有通配符的模式匹配问题一直是研究的热点。针对已有的研究工作中通配符和长度约束具有较强的局限性问题,研究带有灵活通配符的模式匹配问题,其中通配符可以在模式的任意两子串间出现且可以指定灵活的长度约束。采用非线性数据结构—后缀树,设计了求解模式所有解的完备算法PAS\"I'。预处理阶段采用在线增量式算法构建具有文本先验知识的后缀树,搜索阶段结合动态规划的思想,逐个匹配模式中字符,最终得到完备解。在基因序列上的实验表明,PAST比其他算法具有更好的时间性能。     

Engineering a Lightweight Suffix Array Construction Algorithm

In this paper we describe a new algorithm for building the suffix array of a string. This task is equivalent to the problem of lexicographically sorting all the suffixes of the input string. Our algorithm is based on a new approach called deep–shallow sorting: we use a shallow sorter for the suffixes with a short common prefix, and a deep sorter for the suffixes with a long common prefix. All the known algorithms for building the suffix array either require a large amount of space or are inefficient when the input string contains many repeated substrings. Our algorithm has been designed to overcome this dichotomy. Our algorithm is lightweight in the sense that it uses very small space in addition to the space required by the suffix array itself. At the same time our algorithm is fast even when the input contains many repetitions: this has been shown by extensive experiments with inputs of size up to 110 Mb. The source code of our algorithm, as well as a C library providing a simple API, is available under the GNU GPL.     

Tandem repeat查找方法比较

Tandem repeat在基因组成和进化中起到非常重要的作用,查找和分析Tandem repeat已经成为当前生物信息学的一个前沿领域和研究焦点.目前在这一研究领域存在多类解决方法,主要有基于LZ分解技术的方法和最近兴起的基于后缀树索引的方法.本文选取了两种时间复杂度达到O（nlogn）数量级的代表性的方法,对这两种方法进行了全面的综述,并对它们的性能进行了系统的比较和分析.