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Abstract. Typical ontology matching applications, such as ontology integration, focus on the computation of correspondences holding between the nodes of two graph-like structures, e.g., between concepts in two ontologies. However, for applications such as web service integration, we need to establish whether full graph structures correspond to one another globally, preserving certain structural properties of the graphs being considered. The goal of this paper is to provide a new matching operation, called structure-preserving semantic matching. This operation takes two graph-like structures and produces a set of correspondences, (i) still preserving a set of structural properties of the graphs being matched, (ii) only in the case if the graphs are globally similar to one another. Our approach is based on a formal theory of abstraction and on a tree edit distance measure. We have evaluated our solution in various settings. Empirical results show the efficiency and effectiveness of our approach.

As the number of services and the size of data involved in workflows increases, centralised orchestration techniques are reaching the limits of scalability. In the classic orchestration model, all data pass through a centralised engine, which results in unnecessary data transfer, wasted bandwidth and the engine to become a bottleneck to the execution of a workflow. Choreography techniques, although more complex to model offer a decentralised alternative and are the optimal architecture for data-centric workflows; data are passed directly to where they are required, at the next service in the workflow.While orchestration is the dominant architectural approach, there are relatively few choreography languages and even fewer concrete implementations. This papers contributions are twofold. Firstly we argue the case for choreography in data-intensive computing, and demonstrate through workflow patterns the advantages in terms of scalability when a choreography architecture is adopted. Secondly we introduce the Light Weight Coordination Calculus (LCC), a type of process calculus used to formally define choreographies, and the OpenKnowledge framework, a choreography-based architecture, providing the functionality for peers to coordinate in an open peer-to-peer system. Through LCC and the OpenKnowledge framework we practically demonstrate how choreography can be achieved in a lightweight manner with a comparatively simple process language.

International audienceClinical trials are fundamental for medical science: they provide the evaluation for new treatments and new diagnostic approaches. One of the most difficult parts of clinical trials is the recruitment of patients: many trials fail due to lack of participants. Recruitment is done by matching the eligibility criteria of trials to patient conditions. This is usually done manually, but both the large number of active trials and the lack of time available for matching keep the recruitment ratio low. In this paper we present a method, entirely based on standard semantic web technologies and tool, that allows the automatic recruitment of a patient to the available clinical trials. We use a domain specific ontology to represent data from patients' health records and we use SWRL to verify the eligibility of patients to clinical trials