A network of workstation (NOW) can act as a single and scal-able powerful computer by building a parallel and distributed computing platform on top of it. WAKASHI is such a platform system that supports persistent object management and makes full use of resources of NOW for high performance transac-tion processing. 0ne of the main diffculties to overcome is the bottleneck caused by concurrency control mechanism. Therefore, a non-blocking locking method is designed, by adopting several novel techniques to make it outperform the other typi-cal locking methods such as 2PL: 1)an SDG(Semantic Dependency Graph)based non-blocking locking protocol for fast transaction scheduling; 2)a massively virtual memory based backup-page undo algorithm for fast restart; and 3)a multi processor and multi-thread based transaction manager for fast execution. The new mechanismshave been implemented in WAKASHI and the performance comparison experiments with 2PL and DWDL have been done. The results show that the new method can outperform 2PL and DWDL under certain conditions．This is meaningful for choos-ing effective concurrency control mechanisms for improving transaction-processing performance in NOW environments.

Most query languages in XML database systems use Regular Path Expressions (RPE) to query or extract data from databases and some query processing and optimization techniques have been proposed for RPEs. Conceptually XML documents are collections of path instances. Each path instance should conform to an XML element tag sequence, called path schema. A RPE query can be written as an automaton that can represent a language, while path schemas can be seen as sentences. In this paper, a novel RPE computing approach, automaton match (AM), is proposed. AM queries the RPEs by matching the automatons with path schemas. The experimental results show AM is quite efficient for computing RPE queries.

为了提高个性化推荐的质量，简化推荐规则生成过程中相关参数的设置，讨论了应用于个性化推荐中的关联规则的性质,定义了“推荐非空率”这一新的推荐测度以及“1-支持频繁项集”和“k最大关联规则”的概念，提出了“在1-支持频繁项集中生成k最大关联规则”的思想，设计了满足该思想且适合于不同滑动窗口深度下推荐的关联规则挖掘算法。理论分析及实验结果表明，该算法具有最大的推荐非空率、较高的推荐准确率和F-测度，并有效地简化了规则挖掘过程中阈值的设置。

文本聚类随着网上文本的激增以及实际应用中的需求，引起了人们越来越多的重视。通过分析文本的特征以及常用的文本聚类方法，提出了一种对文本进行细致划分获取细化簇、并在细化簇基础上进行聚类的文本聚类方法。在聚类过程中，采用曲线的多项式拟合技术提出了一种自动发现阈值的方法,并把该方法应用于细化簇的寻找步骤中。与凝聚的层次聚类方法的实验比较结果表明，使用自动阈值发现的方法在时间消耗、聚类效果、以及对孤立点的容忍性方面都具有更优的性能。

文章介绍了一个著名的在线事务处理系统性能测试标准TPC-C 及其在面向对象数据库管理系统Fish上的设计与实现，并基于实验结果对影响数据库系统性能的主要因素进行了验证和分析。