Introduction:We see increased use of existing observational data in order to achieve fast and transparent production of empirical evidence in health care research. Multiple databases are often used to increase power, to assess rare exposures or outcomes, or to study diverse populations. For privacy and sociological reasons, original data on individual subjects can’t be shared, requiring a distributed network approach where data processing is performed prior to data sharing.Case Descriptions and Variation Among Sites:We created a conceptual framework distinguishing three steps in local data processing: (1) data reorganization into a data structure common across the network; (2) derivation of study variables not present in original data; and (3) application of study design to transform longitudinal data into aggregated data sets for statistical analysis. We applied this framework to four case studies to identify similarities and differences in the United States and Europe: Exploring and Understanding Adverse Drug Reactions by Integrative Mining of Clinical Records and Biomedical Knowledge (EU-ADR), Observational Medical Outcomes Partnership (OMOP), the Food and Drug Administration’s (FDA’s) Mini-Sentinel, and the Italian network—the Integration of Content Management Information on the Territory of Patients with Complex Diseases or with Chronic Conditions (MATRICE).Findings:National networks (OMOP, Mini-Sentinel, MATRICE) all adopted shared procedures for local data reorganization. The multinational EU-ADR network needed locally defined procedures to reorganize its heterogeneous data into a common structure. Derivation of new data elements was centrally defined in all networks but the procedure was not shared in EU-ADR. Application of study design was a common and shared procedure in all the case studies. Computer procedures were embodied in different programming languages, including SAS, R, SQL, Java, and C++.Conclusion:Using our conceptual framework we found several areas that would benefit from research to identify optimal standards for production of empirical knowledge from existing databases.an opportunity to advance evidence-based care management. In addition, formalized CM outcomes assessment methodologies will enable us to compare CM effectiveness across health delivery settings.

Cloud computing infrastructures support dynamical and flexible access to computational, network and storage resources. To date, several disjoint industrial and academic technologies provide infrastructure level access to Clouds. Especially for industrial platforms, the evolution of de-facto standards goes together with worries about user lock-in to a platform. The Contrail project [6] proposes a federated and integrated approach to Clouds. In this work we present and motivate the architecture of Contrail federations. Contrail's goal is to minimize the burden on the user and increase the efficiency in using Cloud platforms by performing both a vertical and a horizontal integration. To this end, Contrail federations play a key role, allowing users to exploit resources belonging to different cloud providers, regardless of the kind of technology of the providers and with a homogeneous, secure interface. Vertical integration is achieved by developing both the Infrastructure-and the Platform-asa-Service levels within the project. A third key point is the adoption of a fully open-source approach toward technology and standards. Beside supporting user authentication and applications deployment, Contrail federations aim at providing extended SLA management functionalities, by integrating the SLA management approach of SLA@SOI project in the federation architecture.

ASSIST (A Software development System based upon Integrated Skeleton Technology) is a programming environment oriented to the development of parallel and distributed high-performance applications according to a unified approach. The language and implementation features of ASSIST are a result of our long-term research in parallel programming models and tools. ASSIST is evolving towards programming environments for high-performance complex enabling platforms, especially Grids. In this paper, we show how ASSIST can act as a valid research vehicle to study, experiment and realize Grid-aware programming environments for high-performance applications. Special emphasis is put on the innovative methodologies, strategies and tools for dynamically adaptive applications, that represent the necessary step for the success of Grid platforms.First we discuss the conceptual framework for Grid-aware programming environments, based upon structured parallel programming and components technology, anticipating how ASSIST possesses the essential features required by such framework. Then we summarize the ASSIST programming model, showing its evolution, along the line of structured parallel programming, to solve critical problems of expressive power, flexibility, interoperability and efficiency; some examples, both of kernels and of complex applications, are used to point out the ASSIST features. The modular compiler model and the current implementation for heterogeneous platforms and Globus-based Grids are illustrated. We show the features that allow ASSIST programs to be used in CORBA infrastructures, that represents our basic starting point towards interoperability in Grid applications. Finally, the presentation of all the previous issues is used to derive an ASSIST-based model for supporting dynamically adaptive applications.--

Abstract. We present a new result concerning the parallelisation of DBSCAN, a Data Mining algorithm for density-based spatial clustering. The overall structure of DBSCAN has been mapped to a skeletonstructured program that performs parallel exploration of each cluster. The approach is useful to improve performance on high-dimensional data, and is general w.r.t. the spatial index structure used. We report preliminary results of the application running on a Beowulf with good efficiency.