针对“数字海洋”应用目标，分析海洋数据管理与应用需求，研究数据仓库与数据挖掘技术在海洋信息领域的应用现状，提出海洋数据仓库与数据挖掘关键技术问题，以及构建海洋数据仓库及数据挖掘系统技术方法。

XML is rapidly emerging as the dominant standard for exchanging data on the WWW. Most of application data are stored in relational databases due to its popularity and rich development experiences over it. Therefore, how to provide a proper mapping approach from relational data to XML documents becomes an important topic. Integrity constraints are useful for semantic specification that plays the important roles in relation schema definition. The existing XML schema language does not define general constraints and maintaining method for integrity constraints. So how to use XML to express and maintain integrity constraints especially the advanced integrity constraints, e.g., general constraints of relational data is one of challenge research issues. In this paper, a novel mapping approach is proposed to map relation data to XML document with active nodes, XMLA, and extended DTD with constraints, DTDC. The ability to maintain integrity constraints makes our approach more effective than other approaches.

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。

Image thresholding is one of the main techniques for image segmentation. It has many applications in pattern recognition, computer vision, and image and video understanding. This paper formulates the thresholding as an optimization problem: finding the best thresholds that minimize a weighted sum-of-squared-error function. A fast iterative optimization algorithm is presented to reach this goal. Our algorithm is compared with a classic, most commonly-used thresholding approach. Both theoretic analysis and experiments show that the two approaches are equivalent. However, our formulation of the problem allows us to develop a much more efficient algorithm, which has more applications, especially in real-time video surveillance and tracking systems.

With the increasing of data and Web users in the Internet, it is necessary and significant to identify users for offering further possible personalized services. To implement this goal, we used the users' surfing historical data stored in the Web log as features for classification. This paper aims to develop some techniques to achieve this objective using fuzzy neural networks (FNNs). Experimental results demonstrate the effectiveness of our proposed approach.