Abstract: The recent media focus on Smart City services, particularly ride sharing, that provide ordinary users with the ability to advertise their resources has highlighted society’s need for transparent and accountable systems. Current systems offer little transparency behind their processes that claim to provide accountability to and for their users. To address such a concern, some applications provide a static, textual description of the automated algorithms used, with a view to promote transparency. However, this is not sufficient to inform users exactly how information is derived. These descriptions can be enhanced by explaining the actual execution of the algorithm, the data it operated on, and the parameters it was configured with. Such descriptions about a system’s execution and its information flow can be expressed using PROV, a standardised provenance data model. However, given its generic and domain-agnostic nature, PROV only provides limited information about the relationship between provenance elements. Combined with semantic information, a PROV instance becomes a rich resource, which can be exploited to provide users with understandable accounts of automated processes, thereby promoting transparency and accountability. Thus, this paper contributes, a vocabulary for Smart City resource sharing applications, an architecture for accountable systems, and a set of use cases that demonstrate and quantify how the semantics enrich an account in a ride share scenario.

Citation: Heather Packer, Dimitris Diochnos, Michael Rovatsos, Ya’akov Gal, Luc Moreau, Semantics and Provenance for Accountable Smart City Applications, The Role of Semantics in Smart Cities, Semantic Web Journal special issue, 2014.

Download: http://bit.ly/2jcTK9B

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: We use provenance graphs to solve a problem within incentive engineering: motivating humans to accept proposals generated by agents. Across several provenance graphs created within the HAC-ER disaster-management system, we ran retrospectively a bespoke algorithm for subgraph matching in order to extract narrative information from the provenance data. The output of the algorithm comprised a series of text messages which, had they been generated at the time of the disaster trial, would have been transmissable with the specific intention of encouraging participants not to reject certain tasks.

The algorithm found all expected subgraphs within the provenance graphs, on an any-time basis and in a time linearly proportional to the number of nodes. Our algorithm is extendable to other situations in which agents present tasks to humans.

Keywords: Incentive engineering, subgraph matching, task allocation, human-agent collectives, provenance graphs, disaster management.

Citation: Mark Ebden, Trung Dong Huynh, Luc Moreau and Stephen Roberts. Incentive Engineering through Subgraph Matching – with Application to Task Allocation.

Download: http://bit.ly/1S8eHNt

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.

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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.

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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.

Citation: Moreau, Luc and Groth, Paul (2013) Provenance: An Introduction to PROV, Morgan and Claypool, 129pp.

A free sample can be found here: http://www.morganclaypool.com/doi/abs/10.2200/S00528ED1V01Y201308WBE007

Abstract. Provenance is a record that describes the people, institutions, entities, and activities involved in producing, influencing, or delivering a piece of data or a thing. The W3C Provenance Working group has just published the PROV family of specifications, which include a data model for provenance on the Web. The working group introduces a notion of valid PROV document whose intent is to ensure that a PROV document represents a consistent history of objects and their interactions that is safe to use for the purpose of reasoning and other kinds of analysis. Valid PROV documents satisfy certain definitions, inferences, and constraints, specified in PROV-CONSTRAINTS. This paper discusses the design of ProvValidator, an online service for validating provenance documents according to PROV-CONSTRAINTS. It discusses the algorithmic design of the validator, the complexity of the algorithm, how we demonstrated compliance with the standard, and its REST API.

doi: http://eprints.soton.ac.uk/361113/

Citation: Moreau, Luc, Huynh, Trung Dong and Michaelides, Danius (2014) An Online Validator for Provenance: Algorithmic Design, Testing, and API. In, 17th International Conference on Fundamental Approaches to Software Engineering (FASE’14), Springer-Verlag.

Download: http://bit.ly/2itj1eI