Structured parallel programs ought to be conceived as two separate and complementary entities: computation, which expresses the calculations in a procedural manner, and coordination, which abstracts the interaction and communication. By abstracting commonly used patterns of parallel computation, communication, and interaction, algorithmic skeletons enable programmers to code algorithms without specifying platform‐dependent primitives. This article presents a literature review on algorithmic skeleton frameworks (ASKF), parallel software development environments furnishing a collection of parameterizable algorithmic skeletons, where the control flow, nesting, resource monitoring, and portability of the resulting parallel program is dictated by the ASKF as opposed to the programmer. Consequently, the ASKF can be positioned as high‐level structured parallel programming enablers, as their systematic utilization permits the abstract description of programs and fosters portability by focusing on the description of the algorithmic structure rather than on its detailed implementation. Copyright © 2010 John Wiley & Sons, Ltd.

We present an agent-based distributed decision support system for the diagnosis and prognosis of brain tumours developed by the HealthAgents project. HealthAgents is a European Union funded research project, which aims to enhance the classification of brain tumours using such a decision support system based on intelligent agents to securely connect a network of clinical centres. The HealthAgents system is implementing novel pattern recognition discrimination methods, in order to analyse in vivo Magnetic HealthAgents intends not only to apply forefront agent technology to the biomedical field, but also develop the HealthAgents network, a globally distributed information and knowledge repository for brain tumour diagnosis and prognosis.

We explore some recurring socio-technical problems encountered in the development of infrastructure for sharing and re-using data across sites and social scales for eHealth research. We link these problems to contradictions between underlying assumptions about data as a commodity whose reuse is not compromised when it is extracted from the context in which it has been captured, and the reality of data as entangled with, and constituted through, local practice. To illustrate these problems, we draw on the experiences of a number of HealthGrid projects developing infrastructures for data sharing and reuse, and trace the strategies that have evolved to address them. These experiences problematize the "one size fits all" model initially adopted by HealthGrids, and highlight the need for design and development strategies that are able to engage with local needs and thereby ensure that the technical infrastructure is properly aligned with the human infrastructure it is supposed to support.

A disruptive technology often used in finance, Internet of Things (IoT) and healthcare, blockchain can reach consensus within a decentralised network—potentially composed of large amounts of unreliable nodes—and to permanently and irreversibly store data in a tamper-proof manner. In this paper, we present a reputation system for Intelligent Transportation Systems (ITS). It considers the users interested in traffic information as the main actors of the architecture. They securely share their data which are collectively validated by other users. Users can choose to employ either such crowd-sourced validated data or data generated by the system to travel between two locations. The data saved is reliable, based on the providers’ reputation and cannot be modified. We present results with a simulation for three cities: San Francisco, Rome and Beijing. We have demonstrated the impact of malicious attacks as the average speed decreased if erroneous information was stored in the blockchain as an implemented routing algorithm guides the honest cars on other free routes, and thus crowds other intersections.

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