We have released the video above which demonstrates the practical applications of our research in emergency care situations. In this case study, nurses or student nurses wearing a Smart-Assistant (in this example Smart-Eye-Ware) are attempting to resuscitate a patient (doll). Besides offering information on demand in their HMD (e.g. instant feedback, regulations or quick-check information, hints), the Smart-Assistant also detects specific activities like performing chest compressions and provides feedback if the activity is not performed to required standards.

This research expands on work presented in the award winning papers: Smart-Watch Life Saver: Smart-Watch Interactive-Feedback System for Improving Bystander CPR and Recognizing Hospital Care Activities with a Pocket Worn Smartphone (award details here).

Abstract: On the Web, where resources such as documents and data are published, shared, transformed, and republished, provenance is a crucial piece of metadata that would allow users to place their trust in the resources they access. The Open Provenance Model (OPM) is a community data model for provenance that is designed to facilitate the meaningful interchange of provenance information between systems. Underpinning OPM is a notion of directed graph, where nodes represent data products and processes involved in past computations, and edges represent dependencies between them; it is complemented by graphical inference rules allowing new dependencies to be derived. Until now, however, the OPM model was a purely syntactical endeavor. The present paper extends OPM graphs with an explicit distinction between precise and imprecise edges. Then a formal semantics for the thus enriched OPM graphs is proposed, by viewing OPM graphs as temporal theories on the temporal events represented in the graph. The original OPM inference rules are scrutinized in view of the semantics and found to be sound but incomplete. An extended set of graphical rules is provided and proved to be complete for inference. The paper concludes with applications of the formal semantics to inferencing in OPM graphs, operators on OPM graphs, and a formal notion of refinement among OPM graphs.

doi: http://eprints.soton.ac.uk/id/eprint/374183

Citation: Natalia Kwasnikowska, Luc Moreau, and Jan Van den Bussche. A formal account of the open provenance model. ACM Transactions on the Web, February 2015.

Download: http://bit.ly/2j900wX

Abstract: As users become confronted with a deluge of provenance data, dedicated techniques are required to make sense of this kind of information. We present Aggregation by Provenance Types, a provenance graph analysis that is capable of generating provenance graph summaries. It proceeds by converting provenance paths up to some length k to attributes, referred to as provenance types, and by grouping nodes that have the same provenance types. The summary also includes numeric values representing the frequency of nodes and edges in the original graph.Quantitative and qualitative evaluations and a complexity analysis show that this technique is tractable; with small values of k, it can produce useful summaries and can help detect outliers. We illustrate how the generated summaries can further be used for conformance checking and visualization.

doi: http://eprints.soton.ac.uk/id/eprint/364726

Citation: Luc Moreau. Aggregation by provenance types: A technique for summarising provenance graphs. In Graphs as Models 2015 (An ETAPS’15 workshop), London, UK, April 2015.

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

Prof. Moreau gives an overview of his current research interests, explaining the concepts of Reputation and Provenance and how their integration into applications can make the latter more trustworthy for the end user. He illustrates an example in the context of the SmartSociety project and talks about the integration of a Provenance Architecture into Collective Adaptive Systems, and designing a new Reputation System. The interview continues with the topic of how explanations to computations can be provided to users through these technologies, and ends with a discussion on his motivations and expected impact from joining SmartSociety.

You can watch the complete 7 minute interview below, or directly on Youtube, here.