Cooperative distributed architecture for mashups

Since the advent of Web 2.0, personalised applications such as mashups have become widely popular. Mashups enable end-users to fetch data from distributed data sources, and refine it based on their personal needs. This high degree of personalisation that mashups offer comes at the expense of performance and scalability. These scalability challenges are exacerbated by the centralised architectures of current mashup platforms. In this paper, we address the performance and scalability issues by designing CoMaP – a distributed mashup platform. CoMaP’s architecture comprises of several cooperative mashup processing nodes distributed over the Internet upon which mashups can, fully or partially, be executed. CoMaP incorporates a dynamic and efficient scheme for deploying mashups on the processing nodes. Our scheme considers a number of parameters such as variations in link delays and bandwidths, and loads on mashup processing nodes. CoMaP includes effective and low-cost mechanisms for balancing loads on the processing nodes as well for handling node failures. Furthermore, we propose novel techniques that leverage keyword synonyms, ontologies and caching to enhance end-user experience. This paper reports several experiments to comprehensively study CoMaP’s performance. The results demonstrate CoMaP’s benefits as a scalable distributed mashup platform.     

A service-based framework for pharmacogenomics data integration

Data are central to scientific research and practices. The advance of experiment methods and information retrieval technologies leads to explosive growth of scientific data and databases. However, due to the heterogeneous problems in data formats, structures and semantics, it is hard to integrate the diversified data that grow explosively and analyse them comprehensively. As more and more public databases are accessible through standard protocols like programmable interfaces and Web portals, Web-based data integration becomes a major trend to manage and synthesise data that are stored in distributed locations. Mashup, a Web 2.0 technique, presents a new way to compose content and software from multiple resources. The paper proposes a layered framework for integrating pharmacogenomics data in a service-oriented approach using the mashup technology. The framework separates the integration concerns from three perspectives including data, process and Web-based user interface. Each layer encapsulates the heterogeneous issues of one aspect. To facilitate the mapping and convergence of data, the ontology mechanism is introduced to provide consistent conceptual models across different databases and experiment platforms. To support user-interactive and iterative service orchestration, a context model is defined to capture information of users, tasks and services, which can be used for service selection and recommendation during a dynamic service composition process. A prototype system is implemented and cases studies are presented to illustrate the promising capabilities of the proposed approach.