一种精确度加强的主动队列管理算法PEBLUE

主动队列管理是IP拥塞控制的一种重要机制，BLUE算法作为一种典型的主动队列管理算法，使用丢包和连接空闲；件来控制拥塞，但是其性能还不稳定．本文对BI。UE算法进行了改进，提出了一种精确度加强的主动队列管理算法—PE-BLUE（Precision Enhanced BLUE），它能自适应地调整其参数．仿真表明PEBLUE能够进一步提高对队列的控制精确度，改善BLUE算法的性能．     

基于网格技术的大数据样例选择算法研究

传统的大数据样例选择算法由于仅设置单一参数，导致算法在实际应用中的性能较差。因此，提出基于网格技术的大数据样例选择算法。首先，设置大数据样例选择算法的参数，计算出样例精确度的平均值，并在网格技术的支持下，确定样例选择算法的目标函数；其次，通过对样例数据进行聚类，生成大数据样例选择算法；最后，进行仿真实验。仿真结果表明，与传统大数据样例选择算法相比，该算法的压缩比为6.092，在实际应用中的性能更好。     

基于索引的子图查询技术研究进展

图作为表示实体间的数据结构，在社区发现、生物化学分析、社会安全分析等数据关联性要求较高的领域有着广泛的应用。对于大规模数据下进行实时的图查询问题，通过构建合适的索引可以有效降低查询响应时间，提高查询精确度。首先介绍基于索引的子图查询算法的基本结构；然后按索引的构建方式将主流算法分为基于枚举的方法和基于频繁模式挖掘的方法两大类，分别从索引特征、索引结构、应用数据集等方面进行介绍和分析；最后对基于索引的子图查询算法面临的主要问题进行总结和分析，阐述了最新的分布式系统下图查询技术，并对未来趋势进行展望。     

差分隐私的查询一致性约束研究

针对差分隐私直方图发布中区间查询的不一致问题,研究已有需迭代调整的局部最优线性无偏估计算法LBLUE,提出一种不需迭代且满足一致性约束查询的CA算法。通过对1棵添加Laplace噪声的满k-叉区间树进行一致性调整：先利用TDICE算法进行自顶向下的不一致估计,再利用BUCE算法进行自底向上的一致性估计,得到满足一致性约束查询的差分隐私满k-叉区间树,遍历后发布满足一致性约束查询的直方图数据。经过证明和实验分析,一致性调整后的查询区间满足一致性约束查询,且精确度优于Boost-2算法和LBLUE算法的,同时算法的时间效率高于LBLUE算法的。     

XML查询优化模型XQO的研究设计

XML现有的查询技术不够成熟,效率低下,精确度不高,如何优化查询成为业界热点和难点问题。结合当今查询优化算法技术,设计了一个查询优化模型XQO,从查询过程的各个阶段进行优化查询解析、逻辑优化、物理优化,设计执行策略和算法,并从实验结果验证优化的效果。     

道路网中的移动对象连续K近邻查询

已有道路网中的连续k近邻查询处理算法采用增量式的查询处理机制,当数据频繁更新时性能急剧下降.结合多核多线程技术,提出了一种基于多线程的连续查询处理框架.该框架周期性重计算所有查询结果,将查询处理分为顺序执行的数据更新阶段和查询执行阶段,分别使用任务并行和数据并行的方法执行各阶段的操作.设计了数据更新阶段使用的数据结构,提出了查询处理阶段的k近邻查询处理策略,包含离线预计算和在线k近邻查询处理算法两个部分.对k近邻算法复杂性及多线程处理框架的加速比进行了理论分析.实验结果表明,提出的算法在数据频繁更新下,串行执行时性能优于已有算法,而基于多线程处理框架的并行执行在任何参数配置下性能均优于已有算法;且基于多线程处理框架的并行执行具有较好的性能扩展性,加速比可以达到1.51～1.7.     

应用聚类和遗传算法获取模糊模型

针对在复杂系统的模糊建模中，模型的精确度和可解释性很难同时得到满足的问题，利用PNC2聚类算法和遗传算法各自的特点，提出了一种新的建模方法。利用PNC2聚类算法创建初始模型，然后对规则参数进行编码，借助实值遗传算法优化模型。PNC2是有指导的层次凝聚聚类算法，双重的合并测试使获得的初始模型达到局部最优解，具有很强的可解释性；遗传算法通过自适应优化来提高模型的精确度。通过运用Iris数据分类问题，验证了算法的可行性和有效性。     

基于字符树结构的高性能中文词库技术

海量中文信息处理是大数据处理的一个分支,而利用大数据技术进行中文信息处理一定离不开中文分词,所以中文分词技术是大数据中文信息处理的基础性技术.中文分词技术自本世纪以来,一直在性能与精确度两个方向在推进;在性能方面主要以改进分词扫瞄算法,改进词库存储技术与查询方式来提高性能.在精确度上主要是对未登录词与歧义词的甄别与处理方法进行改进.本文摒弃了通过词库索引查询的思想,提出一种基于字符树的词库存储结构.它的分词速度是普通折半法的35倍,占用内存只是它的1/5.它将为大数据技术在处理中文信息时在性能上推进了一大步.     

混合遗传算法和隐马尔可夫模型的Web信息抽取

传统Web信息抽取的隐马尔可夫模型对初值十分敏感和在实际训练中极易得到局部最优模型参数。提出了一种使用遗传算法优化HMM模型参数的Web信息抽取混合算法。该算法使用实数矩阵编码表示染色体,似然概率值为适应度取值,将GA与Baum-Welch算法相结合对HMM模型参数进行全局优化,并且调整GA-HMM的Baum-Welch算法参数实现Web信息抽取。实验结果表明,新的算法在精确度和召回率指标上比传统HMM具有更好的性能。     

嵌入式空间数据库综合查询算法

嵌入式空间数据库一般作为嵌入式GIS的后端,为其提供对空间数据和属性数据的存储、搜索、查询等多项功能。其中,查询性能是直接影响嵌入式GIS运行效率的基本因素之一。该文对嵌入式空间数据库综合查询算法进行分类,提出并实现了先空间串行查询算法、先属性串行查询算法和并行查询算法,对该3种查询算法进行性能测试与比较,并给出了测试比较结果。     

Performance bounds for rate envelope multiplexing

In QoS guaranteed communication networks, such as ATM, several classes of traffic streams with widely varying characteristics share common transmission resources. To achieve high utilization of these networks, while providing appropriate grade of service for all connections, the development of powerful traffic management algorithms is a central issue. Due to scalability reasons traffic control functions like flow, congestion and admission control often need simple and efficient characterization of traffic using mainly aggregate characteristics instead of using information about all the individual flows. In this paper, the saturation probability as a possible performance measure of aggregate traffic on a communication link is discussed. This performance metric, also referred to as the tail distribution of aggregate traffic, is essential in traffic control and management algorithms of high-speed networks including also the prospective QoS Internet. In this paper, using the Chernoff bounding method, efficient closed-form bounds have been derived for the saturation probability for the case when little information is available on the aggregate traffic. The performance of these estimates is also shown by means of numerical examples.     

Fundamental trade-offs in aggregate packet scheduling

In this paper, we investigate the fundamental trade-offs in aggregate packet scheduling for support of guaranteed delay service. In our study, we consider two classes of aggregate packet scheduling algorithms: the static earliest time first (SETF) and dynamic earliest time first (DETF). Through these two classes of aggregate packet scheduling (and together with the simple FIFO packet scheduling algorithm), we show that, with additional timestamp information encoded in the packet header for scheduling purposes, we can significantly increase the maximum allowable network utilization level, while, at the same time, reducing the worst-case edge-to-edge delay bound. Furthermore, we demonstrate how the number of the bits used to encode the timestamp information affects the trade-off between the maximum allowable network utilization level and the worst-case edge-to-edge delay bound. In addition, the more complex DETF algorithms have far superior performance than the simpler SETF algorithms. These results illustrate the fundamental trade-offs in aggregate packet scheduling algorithms and shed light on their provisioning power in support of guaranteed delay service.     

语义缓存的聚集查询匹配研究

为提高海量数据库系统的查询效率，围绕海量数据库系统中的聚集查询技术，把通常应用于小型数据库查询的语义缓存技术拓展到海量数据库的聚集查询中．首先研究了面向聚集查询的语义缓存形式化描述，在此基础上讨论了利用缓存处理查询的条件并对查询匹配进行了分类，提出并实现了包含匹配判定算法和相交匹配判定算法，最后给出了相应的实验结果．在某大型实际工程中的应用表明上述判定算法是有效的．     

一种有效的空间数据仓库区域聚集查询索引结构

空间数据仓库有效地支持对空间数据的管理和分析，提供更加全面的决策支持．讨论了一种有效的空间决策支持手段——空间区域聚集查询的实现．基于aggregate cubetree和aR—tree提出了一个可以有效地在空间维和非空间维上进行区域聚集查询的索引结构aCR-tree及其相关算法，并计算分析了查询算法的时间复杂度．与现有技术相比aCR-tree降低了存储代价和每次查询访问的节点数，通过实验证明，该索引结构可以提供较好的存储性能和查询性能．     

Neural network ensembles: evaluation of aggregation algorithms

Ensembles of artificial neural networks show improved generalization capabilities that outperform those of single networks. However, for aggregation to be effective, the individual networks must be as accurate and diverse as possible. An important problem is, then, how to tune the aggregate members in order to have an optimal compromise between these two conflicting conditions. We present here an extensive evaluation of several algorithms for ensemble construction, including new proposals and comparing them with standard methods in the literature. We also discuss a potential problem with sequential aggregation algorithms: the non-frequent but damaging selection through their heuristics of particularly bad ensemble members. We introduce modified algorithms that cope with this problem by allowing individual weighting of aggregate members. Our algorithms and their weighted modifications are favorably tested against other methods in the literature, producing a sensible improvement in performance on most of the standard statistical databases used as benchmarks.     

Efficient Computation of Iceberg Cubes by Bounding Aggregate Functions

The iceberg cubing problem is to compute the multidimensional group-by partitions that satisfy given aggregation constraints. Pruning unproductive computation for iceberg cubing when nonantimonotone constraints are present is a great challenge because the aggregate functions do not increase or decrease monotonically along the subset relationship between partitions. In this paper, we propose a novel bound prune cubing (BP-Cubing) approach for iceberg cubing with nonantimonotone aggregation constraints. Given a cube over n dimensions, an aggregate for any group-by partition can be computed from aggregates for the most specific n--dimensional partitions (MSPs). The largest and smallest aggregate values computed this way become the bounds for all partitions in the cube. We provide efficient methods to compute tight bounds for base aggregate functions and, more interestingly, arithmetic expressions thereof, from bounds of aggregates over the MSPs. Our methods produce tighter bounds than those obtained by previous approaches. We present iceberg cubing algorithms that combine bounding with efficient aggregation strategies. Our experiments on real-world and artificial benchmark data sets demonstrate that BP-Cubing algorithms achieve more effective pruning and are several times faster than state-of-the-art iceberg cubing algorithms and that BP-Cubing achieves the best performance with the top-down cubing approach.     

Histogram-by: A grouping operator for continuous domains

In this paper, we propose a new operator, histogram-by, which provides a grouping for continuous domains, which partitions records into several groups by given ranges of the target attributes. The histogram-by operator can be represented as histogram-by clause in the SQL statement, and can be easily amenable to query optimization. As the application of the histogram-by operator, we introduce a multi-dimensional histogram query, which returns aggregate values of all ranges specified by the histogram-by clause. To process the query efficiently, we propose effective algorithms using aggregate R-trees. Our experimental results show that our algorithms are reliable in terms of performance over the synthetic and real-world datasets.     

数据仓库系统中一种改进的维层次聚集Cube存储结构

提出利用Cube中的维层次（dimension hierarchy）聚集技术来创建高性能的维层次聚集Cube（dimension hierarchy aggregate cube，DHAC）．充分利用DHAC已保存的维层次信息，对Cube中多维数据的查询和更新效率进行了优化，并且支持Cube的上探、下钻等语义操作．在DHAC中进行数据插入和删除等数据更新时，由下向上用更新前后的差值对受到更新结点影响的所有祖先结点进行增量更新．实现了在插入新维或维层次时不需要重新构建聚集Cube就可以实现Cube的模式更新．对维层次聚集Cube与传统Cube进行了算法性能分析和比较，理论分析和实验结果都表明，所提出的DHAC性能最佳．     

Automated Meta-Control for Adaptable Real-Time Software

The software meta-controller is an online agent responsible for dynamically adapting an application's software configuration, e.g. altering operational modes and migrating tasks, to best accommodate varying runtime circumstances. In distributed real-time applications such adaptations must be carried out in a manner which maintains the schedulability of all critical tasks while maximizing some notion of system value for all other tasks. For large-scale real-time applications, considering all possible adaptations at the task-level is computationally intractable. This paper presents an automated aggregate approach to software meta-control, appropriate for large-scale distributed real-time systems. The aggregate automated meta-control problem is still NP-hard, but it has very practical approximate solutions. Introduced, here, are two very-effective approximation algorithms, QDP and GG, with very reasonable polynomial time complexity. Both algorithms also provide us with upper bounds for optimum system values, useful for deriving absolute, albeit somewhat pessimistic, measures of actual performance. Extensive Monte Carlo analysis is used to illustrate that expected performance for both algorithms is generally suboptimal by no more than a few percent. Our flexible software meta-control model is also shown to be readily applied to a wide range of time-sensitive applications.     

时间序列数据立方的存储与聚集计算

本文讨论了从时序数建造、存储数据立方,以及聚集计算的算法,其中Ｎ２３算法和扩展的ＥＮ２３算法可以方便地将一个Ｎ（Ｎ〉３）维数据方立转换为三维数据立方,大大降低了Ｉ／Ｏ次数,极大地提高了运行效率。