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Semantic Web technologies have shown to have great potential in many different domains, to facilitate knowledge representation, exchange and reasoning, in a formal and yet both human and machine understandable way. In particular, within the health domain, they enable knowledge integration and understanding by explicitly defining and linking concepts and relationships using ontologies to information within clinical knowledge bases. This additional metadata also allows for automated decision support and semantic based analytics to be implemented, that facilitate improved healthcare at a lower cost. Unfortunately many existing datasets in healthcare environments are still stored in relational databases, as opposed to using semantic technologies. Due to this, the link with explicit metadata is often lacking or non-existent. Furthermore, both the databases and the clinical terminologies can be considerably large, making the mapping and subsequent uses of the information a difficult process. In a full fledged decision support system the level and accuracy of the mapping can greatly influence the effectiveness of any subsequent analysis and decision support tasks. This is especially true in clinical scenarios, where very large and complex sets of terms need to be mapped to relational databases. In this paper we aim to provide a general approach for interlinking relational data with clinical ontology based metadata that allows for a fine grade evaluation, with respect to the mapping's impact on analytics. We evaluate our approach by mapping information from clinical terminologies, such as SNOMED CT, to a large laboratory dataset contained in a relational database, with the goal of creating a full fledged, semantically enabled, analytics and decision support system.

Semantic Web technologies are used in a variety of domains for their ability to facilitate data integration, as well as enabling expressive, standards-based reasoning. Deploying Semantic Web reasoning processes directly on mobile devices has a number of advantages, including robustness to connectivity loss, more timely results, and reduced infrastructure requirements. At the same time, a number of challenges arise as well, related to mobile platform heterogeneity and limited computing resources. To tackle these challenges, it should be possible to benchmark mobile reasoning performance across different mobile platforms, with rule-and datasets of varying scale and complexity and existing reasoning process flows. To deal with the current heterogeneity of rule formats, a uniform rule-and data-interface on top of mobile reasoning engines should be provided as well. In this paper, we present a cross-platform benchmark framework that supplies 1) a generic, standards-based Semantic Web layer on top of existing mobile reasoning engines; and 2) a benchmark engine to investigate and compare mobile reasoning performance.

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