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The architecture has been used to implement a specific query model that can express complex eligibility criteria and access three diverse data warehouses thus demonstrating the feasibility of this approach in dealing with temporal reasoning and data heterogeneity.

Typically the evaluation of Information Retrieval (IR) systems is focused upon two main system attributes: efficiency and effectiveness. However, it has been argued that it is also important to consider accessibility, i.e. the extent to which the IR system makes information easily accessible. But, it is unclear how accessibility relates to typical IR evaluation, and specifically whether there is a trade-off between accessibility and effectiveness. In this poster, we empirically explore the relationship between effectiveness and accessibility to determine whether the two objectives i.e. maximizing effectiveness and maximizing accessibility, are compatible, or not. To this aim, we empirically examine this relationship using two popular IR models and explore the trade-off between access and performance as these models are tuned.

The provenance of a piece of data refers to knowledge about its origin, in terms of entities and actors involved in its creation, e.g. data sources used, operations carried out on them, and users enacting those operations. Provenance is used to better understand the data and the context of its production, and to assess its reliability, by asserting whether correct procedures were followed. Providing evidence for validating research is of particular importance in the biomedical domain, where the strength of the results depends on the data sources and processes used. In recent times, previously manual processes have become fully or semi-automated, e.g. clinical trial recruitment, epidemiological studies, diagnosis making. The latter is typically achieved through interactions of heterogeneous software systems in multiple settings (hospitals, clinics, academic and industrial research organisations). Provenance traces of these software need to be integrated in a consistent and meaningful manner, but since these software systems rarely share a common platform, the provenance interoperability between them has to be achieved on the level of conceptual models. It is a non-trivial matter to determine where to start in making a biomedical software system provenance-aware. In this paper, we specify recommendations to developers on how to approach provenance modelling, capture, security, storage and querying, based on our expe- * riences with two large-scale biomedical research projects: Translational Research and Patient Safety in Europe (TRANSFoRm) and Electronic Health Records for Clinical Research (EHR4CR). While illustrated with concrete issues encountered, the recommendations are sufficiently high level so as to be reusable across the biomedical domain.

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