存储最优的连续MAX查询方法

考虑滑动窗口数据流上的连续MAX查询问题,提出了一种存储最优的方法.该方法只需保存数据集中可能成为极大值点的部分数据(候选点集),而将所有完全不可能成为极值点的数据进行丢弃,从而可以有效降低存储开销;提出了一个动态维护候选点集的方法,当数据独立分布时,处理一个新到数据所需的时间为O(1og N),处理数据失效的时间开销为O(1),其中N为滑动窗口包含的全部数据数目.理论分析和实验结果表明了该方法能够适应流速很快的场景,具有较高的效率.     

Improved Approximate Detection of Duplicates for Data Streams Over Sliding Windows

Detecting duplicates in data streams is an important problem that has a wide range of applications. In general,precisely detecting duplicates in an unbounded data stream is not feasible in most streaming scenarios,and,on the other hand,the elements in data streams are always time sensitive. These make it particular significant approximately detecting duplicates among newly arrived elements of a data stream within a fixed time frame. In this paper,we present a novel data structure,Decaying Bloom Filter(DBF),as an extension of the Counting Bloom Filter,that effectively removes stale elements as new elements continuously arrive over sliding windows. On the DBF basis we present an efficient algorithm to approximately detect duplicates over sliding windows. Our algorithm may produce false positive errors,but not false negative errors as in many previous results. We analyze the time complexity and detection accuracy,and give a tight upper bound of false positive rate. For a given space G bits and sliding window size W,our algorithm has an amortized time complexity of O((G/W))~(1/2). Both analytical and experimental results on synthetic data demonstrate that our algorithm is superior in both execution time and detection accuracy to the previous results.     

基于能量和频繁模式的数据流预测查询算法

设计了数据流预测查询的新模型,包括局域流能量预测、能量分布模式挖掘及预测序列的重构和数据流能量的度量方法;设计了融合数据流能量回归与基于频繁模式的小波分解预测新方法,并将新算法推广到强偶合多数据流的预测查询;提出了最近最频繁序列模式的新概念,并应用于局域流能量分解;在真实数据上的模拟实验,验证了算法的有效性.     

基于P2P的Web搜索技术

Web搜索引擎已经成为人们从海量Web信息中快速找到所需信息的重要工具,随着Web数据量的爆炸性增长,传统的集中式搜索引擎已经越来越不能满足人们不断增长的信息获取需求.随着对等网络(peer-to-peer,简称P2P)技术的快速发展,人们提出了基于P2P的Web搜索技术并迅速成为研究热点.研究的目的是对现有的基于P2P的Web搜索技术进行总结,以期为进一步研究指明方向.首先分析了基于P2P的Web搜索面临的诸多挑战;然后重点总结分析了基于P2P的Web搜索的各项关键技术的研究现状,包括系统拓扑结构、数据存放策略、查询路由机制、索引切分策略、数据集选择、相关性排序、网页收集方法等;最后对已有的3个较有特色的基于P2P的Web搜索原型系统进行了介绍.     

DTD约束下的XML树模式查询最小化

目前大部分XML查询语言都使用树模式来匹配待查询的XML文档树以得到所需要的、与模式树相吻合的查询结果,此效率在很大程度上取决于XML模式树的大小,那么尽可能快速地查找并删除查询模式树中的冗余节点就变得十分重要。重点讨论DTD约束下树模式的最小化问题,将DTD兄弟约束SC拓展成扩展兄弟约束ESC,使其能够表达DTD约束中的祖先-后代关系;并指出只包含{ESC,/,//,[],*}的查询树模式的最小化问题的复杂度是指数级的,且当模式树是分支受限的时候,其最小化问题的复杂度是多项式时间的;最后给出了一个多项式时间的受限分支的模式树最小化算法。     

布鲁姆过滤器查询算法

从理论和应用两方面系统地综述了布鲁姆过滤器查询算法迄今为止的主要研究成果,分析了目前布鲁姆过滤器查询算法的研究现状,最后展望了布鲁姆过滤器查询算法未来可能的研究方向.     

提高P2P下top-k搜索性能的研究

分析了P2P中节点资源分布特点。根据搜索条件,在资源匹配度的基础上提出了节点匹配度的概念。基于节点匹配度与资源的smallworld分布特征提出top-k资源的搜索、评价算法。该算法使搜索能够在整个网络内进行,并朝资源匹配高的范围传播。在提高搜索效率、节约网络带宽的同时,保证了最终获取的k个资源是最匹配的。根据搜索条件选择广播匹配节点的方法有效地平衡了搜索、评价的带宽和计算资源。     

基于文档与搜索结果上下文的查询扩展方法

在查询扩展方法中,如果通过查询结果中关键词的上下文来计算候选关键词的权重,将权重大的词作为查询扩展词,其候选关键词来源于文档中关键词的上下文,这种方法存在主题漂移的问题。为了解决这个问题,提出一种将初始查询结果过滤,只选择与源文档语境相似的搜索结果,来帮助选择查询扩展词的方法。实验结果表明该方法能获得更合适的查询扩展词。     

Pattern discovery and change detection of online music query streams

Mining of music data is one of the most important problems in multimedia data mining. In this paper, two research issues of mining music data, i.e., online mining of music query streams and change detection of music query streams, are discussed. First, we proposed an efficient online algorithm, FTP-stream (Frequent Temporal Pattern mining of streams), to mine all frequent melody structures over sliding windows of music melody sequence streams. An effective bit-sequence representation is used in the proposed algorithm to reduce the time and memory needed to slide the windows. An effective list structure is developed in the FTP-stream algorithm to overcome the performance bottleneck of 2-candidate generation. Experiments show that the proposed algorithm FTP-stream only needs a half of memory requirement of original melody sequence data, and just scans the music query stream once. After mining frequent melody structures, we developed a simple online algorithm, MQS-change (changes of Music Query Streams), to detect the changes of frequent melody structures in current user-centered music query streams. Two music melody structures (set of chord-sets and string of chord-sets) are maintained and four melody structure changes (positive burst, negative burst, increasing change and decreasing change) are monitored in a new summary data structure, MSC-list (a list of Music Structure Changes). Experiments show that the MQS-change algorithm is an effective online method to detect the changes of music melody structures over continuous music query streams.

A hybrid clustering and graph based algorithm for tagSNP selection

TagSNP selection, which aims to select a small subset of informative single nucleotide polymorphisms (SNPs) to represent the whole large SNP set, has played an important role in current genomic research. Not only can this cut down the cost of genotyping by filtering a large number of redundant SNPs, but also it can accelerate the study of genome-wide disease association. In this paper, we propose a new hybrid method called CMDStagger that combines the ideas of the clustering and the graph algorithm, to find the minimum set of tagSNPs. The proposed algorithm uses the information of the linkage disequilibrium association and the haplotype diversity to reduce the information loss in tagSNP selection, and has no limit of block partition. The approach is tested on eight benchmark datasets from Hapmap and chromosome 5q31. Experimental results show that the algorithm in this paper can reduce the selection time and obtain less tagSNPs with high prediction accuracy. It indicates that this method has better performance than previous ones.