HAPS: Supporting Effective and Efficient Full-Text P2P Search with Peer Dynamics

Recently, peer-to-peer (P2P) search technique has become popular in the Web as an alternative to centralized search due to its high scalability and low deployment-cost. However, P2P search systems are known to suffer from the problem of peer dynamics, such as frequent node join/leave and document changes, which cause serious performance degradation. This paper presents the architecture of a P2P search system that supports full-text search in an overlay network with peer dynamics. This architecture, namely HAPS, consists of two layers of peers. The upper layer is a DHT (distributed hash table) network interconnected by some super peers (which we refer to as hubs). Each hub maintains distributed data structures called search directories, which could be used to guide the query and to control the search cost. The bottom layer consists of clusters of ordinary peers (called providers), which can receive queries and return relevant results. Extensive experimental results indicate that HAPS can perform searches effectively and efficiently. In addition, the performance comparison illustrates that HAPS outperforms a flat structured system and a hierarchical unstructured system in the environment with peer dynamics.     

面向Web活跃用户的树型访问模式挖掘算法

传统Web挖掘技术面向所有Web用户,而访问网站时活跃用户与非活跃用户表现特征不同.基于此,提出一种面向活跃用户的访问模式挖掘方法,包括活跃用户会话提取算法(AUSM)和树型访问模式挖掘算法(WAPBUM).AUSM扫描一遍日志数据即可挖掘Web活跃用户并提取会话信息,在提取的用户会话信息基础上,利用网站拓扑结构给出了一种基于树结构的频繁访问模式挖掘算法(WAPBUM).WAPBUM针对Web日志挖掘特点,通过对子树构造等价类,自下而上产生频繁子树.人工数据集和真实数据集上的实验都证明AUSM算法的运行时间与Web日志数据量成线性关系,且运行过程中内存保持稳定;WAPBUM在处理带根子树挖掘时明显快于FREQT算法,所挖掘结果可有效应用于网站结构分析.     

基于权重哈尔小波的XML包含连接估计方法

针对可扩展标记语言（XML）基本查询操作符--包含连接,提出了一种基于权重哈尔小波的结果数估计方法.该方法利用哈尔小波有效压缩XML包含连接结果统计,并通过小波摘要维护统计信息.在估计阶段,使用小波系数重构包含连接结果数.为了减小估计误差,提出基于标签名查询频率的权重模型,并集成于哈尔小波估计方法中.实验证明,对于XML包含连接结果数估计,权重哈尔小波估计方法优于先前的估计方法（如直方图法、随机取样法）.在相同的空间限制下,权重小波估计具有更小的平均相对误差.     

具有抗攻击能力的信誉与信任模型

针对互联网第三方交易平台中的信誉体系容易受到欺诈行为攻击,以及不能真实反映买卖双方信任度的问题,提出一种基于社会网络的具有抗攻击能力的信誉与信任模型.基于交易者评分建立信誉评估算法,通过信任链传递算法建立基于社会网络的信任评估模型,并采用二维聚类过滤算法实现对欺诈数据的排查.通过引入纠偏参数,实现信誉值的客观化与信任值的主观化,为交易者做出最终交易提供决策帮助.实验表明,该信誉与信任模型能够客观反映交易者的服务质量以及交易双方的信任关系,并对不诚实反馈和串通欺骗等攻击行为具有较高的抵抗能力.     

基于多层BiLSTM和改进粒子群算法的应用负载预测方法

为了解决常用时序预测算法精度不高和调参困难的问题,提出基于多层双向长短期记忆（BiLSTM）神经网络的负载预测方法,包括网络模型设计、自适应参数设置和改进粒子群算法优化等步骤. 将数据输入网络模型中进行训练,使用自适应算法进行自动调参;采用基于基准模型的多指标融合的模型评价方法,计算改进粒子群算法的适应度;使用改进粒子群算法优化模型的预测结果. 通过与多种典型时间序列预测算法的实验对比,方法的预测平均绝对百分比误差减小3.6%~7.2%,训练时间缩短10%以上,实验结果验证了方法在时间序列预测中具有更高的准确性和很强的适用性,为使用负载预测结果进行弹性扩缩容提供了重要的科学依据.     

HAPS:Supporting Effective and Effcient Full-Text P2P Search with Peer Dynamics

Recently, peer-to-peer (P2P) search technique has become popular in the Web as an alternative to centralized search due to its high scalability and low deployment-cost. However, P2P search systems are known to suffer from the problem of peer dynamics, such as frequent node join/leave and document changes, which cause serious performance degradation. This paper presents the architecture of a P2P search system that supports full-text search in an overlay network with peer dynamics. This architecture, namely H...