The increasing volume of data describing human disease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the @neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system's architecture is generic enough that it could be adapted to the treatment of other diseases. Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers clinicians the tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medical researchers gain access to a critical mass of aneurysm related data due to the system's ability to federate distributed information sources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access and work on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand for performing computationally intensive simulations for treatment planning and research.

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Across a wide variety of fields, huge datasets are being collected and accumulated at a dramatical pace. The datasets addressed by individual applications are very often heterogeneous and geographically distributed, and are used for collaboration by the communities of users, which are often large and also geographically distributed. There are major challenges involved in the efficient and reliable storage, fast processing, and extracting descriptive and predictive knowledge from this great mass of data. In this paper, we describe design principles and a service based software architecture of a novel infrastructure for distributed and high-performance data mining in Grid environments.

Abstract. Earth and life sciences are at the forefront to successfully include computational simulations and modeling. Medical applications are often mentioned as the killer applications for the Grid. The complex methodology and models of Traditional Chinese Medicine offer different approaches to diagnose and treat a persons health condition than typical Western medicine. A possibility to make this often hidden knowledge explicit and available to a broader audience will result in mutual synergies for Western and Chinese medicine as well as improved patient care. This paper proposes the design and implementation of a method to accurately estimate blood glucose values using a novel non-invasive method based on electro-transformation measures in human body meridians. The framework used for this scientific computing collaboration, namely the ChinaAustria Data Grid (CADGrid) framework, provides an Intelligence Base offering commonly used models and algorithms as Web/Grid-Services. The controlled execution of the Non-Invasive Blood Glucose Measurement Service and the management of scientific data that arise from model execution can be seen as the first application on top of the CADGrid.