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Component-based development is widely used in large-scale software systems, which is focused on modularization and reusability. Each component has several related interfaces, which clearly describe the function, behavior and other relevant information. In addition, component interface also defines communications with other modules. Thus the interface defines all information of the component. The failure of the interface could cause serious problems for the whole system. Therefore, the component interface testing is an essential part of the component development. In this paper, a XML-based component interface testing method is proposed in a virtual experiment platform. The virtual experiment is applied to visualize material property data. The results show its efficiency and flexibility.

“E-science” refers to the use of advanced computing technologies to support computationally intensive and data intensive application [1]. Global collaborations of people and shared resources become reality[2]. In recent years, Grid Computing technology makes it possible to enable e-science to become a new generation infrastructure for scientific cooperation based on Internet[3]. “Data grid” proposed by Ian Foster[4] is used to solve the problem of large-scale data resource sharing in scientific research. DataGrid project and MyGrid project are on behalf of data grid technology for E-science [5] [6]. GIS is a Mature technology [7], but integrating grid and GIS technology is new research hotspot in e-Science application. Shen Z. et al. [8], Sun Q. et al. [9], Deng et al. [10], and Huang Z. et al. [11] have done many related works.

This paper presents TP-PB algorithm. It applies two phases association rule mining one based partition and Bitset for massive remote sensing images data. Firstly, this algorithm divides massive database into several independent blocks logically. Boolean values are stored in the compressed BitSet in each of block. It generates frequent itemsets by Bitset logical AND operation replaces database scans for each of block. Then it unifies the frequent itemsets and generates global candidate itemsets for the whole data. At last it calculates the min-support of these global candidate itemsets and generates global frequent itemsets for the whole data. By dividing the massive data into a number of blocks, it enhance generic application of algorithm ; Besides it increases algorithm efficiency that database scans is replaced by Bitset logical AND operation in each of block, especially for massive remote sensing image data. And the algorithm has been applied to remote sensing images mining association rules.