自动文本分类技术是一种对电子文档进行管理的有效方法，到现在为止，国内外学者提出了许多文本分类的方法，其中kNN和Rocchio是两种比较常见的方法。kNN的特点是有很好的分类效果，但是分类的效率比较差；而Rocchi。的特点是有很好的分类效率，但是分类效果却不理想。首先对kNN和Rocchio的算法进行深入的研究和比较，然后提出了一种新的基于kNN和Rocchio的文本分类方法，命名为Rocchi>kNN方法。这种分类方法先用Rocchio为测试文档产生候选类别，然后再用kNN从候选类别中为测试文档选择出最终类别。这种方法既有kNN分类效果好的特点，又有接近Rocchio的分类效率。同时，还研究了用两种不同的方法来决定候选类别的数目。在中文文档库上的实验表明，新的文本分类方法的效果比单独的kNN和Rocchio的效果都要好，同时分类效率要比kNN好并且接近Rocchio。

XML管理系统的查询处理效率很大程度上取决于系统中XML 数据的存储模式。在用户查询已知或可预测的情况下，根据用户查询设计存储模式可以改善系统的查询处理效率。该文介绍VXMLR系统存储模式的自适应调整机制。根据历史查询信息，VXMLR系统对其存储模式进行自适应调整，从而提高查询处理效率。其基本思路是:首先根据历史查询，推导出适当的映射规则，得到XML 文档在关系数据库中的存储模式;然后，在给定的空间约束下，根据历史查询使用背包问题求解算法选择关系表进行垂直分割或冗余存储相关数据，使查询所访问的无关数据尽可能少.VXMLR 系统提供四种存储模式调整策略，其中两种策略可以实现自适应的存储模式调整。实验结果表明文中提出的方法是有效的.

In this paper,a self-adaptive query routing strategy is introduced for resource location in F2P environment. Each peer builds up query routing table based on contribution on each query topic from its neighbors, guides efficient and effective routing hints for similar queries, and improves traditional query routing strategies based on DFS and BFS in P2P systems.

Query processing is a crucial problem for the success of data management in P2P systems. In this paper, a multi-phase and distributed view selection strategy is introduced. It is proved that in P2P environment view selection can boost query processing efficiency and effectiveness.

Query processing in P2P environment is gaining more and more attention in database research community, and most of the P2P-based query processing systems only consider the processing strategies. However, estimating the cost of query processing is crucial to clarify which factors will affect the efficiency of query processing, and how to optimize the query processing. In this paper, a model is proposed to estimate the cost for query processing in P2P systems. Methods in the model do not depend on the global information in the network, and they only need a small quantity of data transferring to complete the estimation. Experimems are presented to evaluate the efficiency and the effectiveness of the estimation methods.