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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.

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SUMMARYSoftware is changed frequently during its life cycle. New requirements come, and bugs must be fixed. To update an application, it usually must be stopped, patched, and restarted. This causes time periods of unavailability, which is always a problem for highly available applications. Even for the development of complex applications, restarts to test new program parts can be time consuming and annoying. Thus, we aim at dynamic software updates to update programs at runtime. There is a large body of research on dynamic software updates, but so far, existing approaches have shortcomings either in terms of flexibility or performance. In addition, some of them depend on specific runtime environments and dictate the program's architecture. We present JAVADAPTOR, the first runtime update approach based on Java that (a) offers flexible dynamic software updates, (b) is platform independent, (c) introduces only minimal performance overhead, and (d) does not dictate the program architecture. JAVADAPTOR combines schema changing class replacements by class renaming and caller updates with Java HotSwap using containers and proxies. It runs on top of all major standard Java virtual machines. We evaluate our approach's applicability and performance in non-trivial case studies and compare it with existing dynamic software update approaches. Copyright

Abstract. Domain Specific Languages (DSLs) are widely adopted to capitalize on business domain experiences. Consequently, DSL development is becoming a recurring activity. Unfortunately, even though it has its benefits, language development is a complex and time-consuming task. Languages are commonly realized from scratch, even when they share some concepts and even though they could share bits of tool support. This cost can be reduced by employing modern modular programming techniques that foster code reuse. However, selecting and composing these modules is often only within the reach of a skilled DSL developer. In this paper we propose to combine modular language development and variability management, with the objective of capitalizing on existing assets. This approach explicitly models the dependencies between language components, thereby allowing a domain expert to configure a desired DSL, and automatically derive its implementation. The approach is tool supported, using Neverlang to implement language components, and the Common Variability Language (CVL) for managing the variability and automating the configuration. We will further illustrate our approach with the help of a case study, where we will implement a family of DSLs to describe state machines.