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.

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.

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.

Most current Grid security techniques concentrate on traditional security aspects such as authentication, authorization, etc. While they have shown their usefulness, the significance of the information hidden in the historical data corpus denoting the user-Grid interactions has been largely neglected. In fact, such information provide great insight into Grid security and if properly harnessed, will help better protect the Grid against potential attacks. To utilize these hidden information in a service-oriented Grid environment, we propose a hybrid machine learning and statistical model. The machine learning component predicts the security of a service by considering the probability distribution of the past services, while the statistical component evaluates a service's security statistically based on its own past behaviors and users' opinions. We construct an overall architecture based on this hybrid model and demonstrate through examples its effectiveness and potential too er stronger security to the Grid.