Abstract: In computing science, much attention has been paid to generic methods for sharing data in secure infrastructures. These sorts of methods and infrastructures are, of course, necessary for sharing healthcare data. The authors are, however, a long way away from being able to realise the potential of medical and healthcare data to support the sorts of extensive, data-intensive experiments being demanded by precision and stratified medicine. A key architectural problem remaining to be solved is how to maintain control of patient data within the governance of local data jurisdictions, while also allowing these jurisdictions to engage with experiment designs that (because of the need to scale to large population sizes) may require analyses across several jurisdictions. This study provides a snapshot of architectural work underway to provide a clear, effective structure of data safe havens within jurisdictions. It then describes how formally specified experiment designs can be used to enable jurisdictions to work together on experiments that no single jurisdiction could tackle alone. The authors’ current work relates to two jurisdictions (in Scotland and in Italy), but the architecture and methods are general across similar jurisdictions.

Citation: David Robertson, Fausto Giunchiglia, Stephen Pavis, Ettore Turra, Gabor Bella, Elizabeth Elliot, Andrew Morris, Malcolm Atkinson, Gordon McAllister, Areti Manataki, Petros Papapanagiotou, and Mark Parsons (2016). Healthcare data safe havens: towards a logical architecture and experiment automation. The Journal of Engineering, Institution of Engineering and Technology, October, 2016. This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), http://digital-library.theiet.org/content/journals/10.1049/joe.2016.0170.

Download: http://bit.ly/2j3khFT

Abstract: Today’s crowdsourcing systems are predominantly used for processing independent tasks with simplistic coordination. As such, they offer limited support for handling complex, intellectually and organizationally challenging labour types, such as software development. In order to support crowdsourcing of the software development processes, the system needs to enact coordination mechanisms which integrate human creativity with machine support. While workflows can be used to handle highly-structured and predictable labour processes, they are less suitable for software development methodologies where unpredictability is an unavoidable part the process. This is especially true in phases of requirement elicitation and feature development, when both the client and development communities change with time. In this paper we present models and techniques for coordination of human workers in crowdsourced software development environments. The techniques augment the existing Social Compute Unit (SCU) concept-a general framework for management of ad-hoc human worker teams-with versatile coordination protocols expressed in the Lightweight Social Calculus (LSC). This approach allows us to combine coordination and quality constraints with dynamic assessments of software-user’s desires, while dynamically choosing appropriate software development coordination models.

Citation: Dave Murray-Rust, Ognjen Scekic, Petros Papapanagiotou, Hong-Linh Truong, Dave Robertson, Schahram Dustdar: A Collaboration Model for Community-Based Software Development with Social Machines. EAI Endorsed Trans. Collaborative Computing 1(5): e6 (2015).

Download: http://bit.ly/2iMIuNU