Even with a support tool, setting up a Web services security (WS-Security) configuration can be difficult for people who are not familiar with WS-Security. Some of the reasons are that the WS-Security is a very rich specification with many options and often the processing is complicated. This paper introduces an application programming model (WSSAPI) to simplify the programming experience for end users. It was designed by looking at WS-Security processing from an abstract level. Also, it is designed to consider correctness, efficiency, usability, flexibility, portability, and extensibility. End users just follow the six-step programming model provided in WSSAPI to configure WS-Security. The comparison of WSSAPI to others, like WSS4J, WSE, and JSR-105 shows that it is much easier for end users to use WSSAPI. In this paper, existing APIs for WS-Security are reviewed and compared with WSSAPI to evaluate the ease of use and utility.

Model-driven security is a framework to configure WS-security easily. It generates a security policy written in WS-security policy to be transformed into platform-specific configuration files. Since the WS-security policy specification is quite complicated, it is difficult to directly map between a security policy and a configuration. We propose a generic security policy transformation framework using an intermediate model. The intermediate model structure is designed based on the WS-security message structure, because both a security policy and the configuration files correspond to one WS-security message, even though the WS-security policy is flexible in specifying security requirements. Our contributions are simpler transformation rules compared to direct mapping, the support for various platforms, and more flexible updates if the WS-security policy specification changes. We demonstrate the transformation using the intermediate model for WebSphere application server 6.0.

This paper presents a technique for the representation of large-scale hierarchical data which aims to provide good overviews of complete structures and the content of the data in one display space. The technique represents the data by using nested rectangles. It first packs icons or thumbnails of the lowest-level data and then generates rectangular borders that enclose the packed data. It repeats the process of generating rectangles that enclose the lower-level rectangles until the highest-level rectangles are packed. This paper presents two rectangle-packing algorithms for placing items of hierarchical data onto display spaces. The algorithms refer to Delaunay triangular meshes connecting the centers of rectangles to find gaps where rectangles can be placed. The first algorithm places rectangles where they do not overlap each other and where the extension of the layout area is minimal. The second algorithm places rectangles by referring to templates describing the ideal positions for nodes of input data. It places rectangles where they do not overlap each other and where the combination of the layout area and the distances between the positions described in the template and the actual positions is minimal. It can smoothly represent time-varying data by referring to templates that describe previous layout results. It is also suitable for semantics-based or design-based data layout by generating templates according to the semantics or design.

Visualization of distributed processes is useful for the management of large-scale distributed computing systems. Reactivity and scalability are especially important requirements for such visualization of distributed processes. We have proposed the visualization technique "Data Jewelry Box" algorithm, which satisfies both of the above requirements. The technique can be applied for the visualization of distributed processes, however, the algorithm has a problem that may yield much different data layouts even among very similar datasets. This is a serious issue for the seamless visualization of time-varying data. To solve the problem, we propose the extension of "Data Jewelry Box" algorithm. The extension places data elements referring positions of the previous data layout, so that the extension can yield similar layouts among similar datasets. We introduce the extended algorithm, and propose the visualization system for distributed computing systems using the extended algorithm.