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The Open Provenance Architecture (OPA) approach to the challenge was distinct in several regards. In particular, it allows different components of the challenge workflow to independently record documentation, and for the workflow to be executed in different environments, made possible by an open, well-defined data model and architecture. Another noticeable feature is that we distinguish between the data recorded about what has occurred, process documentation, and the provenance of a data item, which is all that caused the data item to be as it is. In this view, provenance is obtained as the result of a query over process documentation. This distinction allows us to tailor the system to best address the separate requirements of recording and querying documentation. Other notable features include the explicit recording of causal relationships between both events and data items, an interaction-based world model, intensional definition of data items in queries rather than relying on explicit naming mechanisms, and styling of documentation to support non-functional application requirements such as reducing storage costs or ensuring privacy of data. In this paper, we describe how each of these features aid us in answering the challenge’s provenance queries.

Provenance refers to the past processes that brought about a given (version of an) object, item or entity. By knowing the provenance of data, users can often better understand, trust, reproduce, and validate it. A provenance-aware application has the functionality to answer questions regarding the provenance of the data it produces, by using documentation of past processes. PrIMe is a software engineering technique for adapting application designs to enable them to interact with a provenance middleware layer, thereby making them provenance-aware. In this article, we specify the steps involved in applying PrIMe, analyse its effectiveness, and illustrate its use with two case studies, in bioinformatics and medicine.

Abstract. Electronic contracts are a means of representing agreed responsibilities and expected behaviour of autonomous agents acting on behalf of businesses. They can be used to regulate behaviour by providing negative consequences, penalties, where the responsibilities and expectations are not met, i.e. the contract is violated. However, long-term business relationships require some flexibility in the face of circumstances that do not conform to the assumptions of the contract, that is, mitigating circumstances. In this paper, we describe how contract parties can represent and enact policies on mitigating circumstances. As part of this, we require records of what has occurred within the system leading up to a violation: the provenance of the violation. We therefore bring together contract-based and provenance systems to solve the issue of mitigating circumstances. 1

Through technologies such as RSS (Really Simple Syndication), Web Services, and AJAX (Asynchronous JavaScript And XML), the Internet has facilitated the emergence of applications that are composed from a variety of services and data sources. Through tools such as Yahoo Pipes, these “mash-ups ” can be composed in a dynamic, just-in-time manner from components provided by multiple institutions (i.e. Google, Amazon, your neighbour). However, when using these applications, it is not apparent where data comes from or how it is processed. Thus, to inspire trust and confidence in mash-ups, it is critical to be able to analyse their processes after the fact. These trailing analyses, in particular the determination of the provenance of a result (i.e. the process that led to it), are enabled by process documentation, which is documentation of an application’s past process created by the components of that application at execution time. In this paper, we define a generic conceptual data model that supports the autonomous creation of attributable, factual process documentation for dynamic multi-institutional applications. The data model is instantiated using two Internet formats, OWL and XML, and is evaluated with respect to questions about the provenance of results generated by a complex bioinformatics mash-up.

Abstract. Evidence-based policy assessment requires evidence from a variety of sources (quantitative and qualitative) to be gathered and then synthesised to form an evaluation of a policy’s aims or outcomes. In this paper we argue that an appropriate provenance framework is an essential pre-requisite for any eSocial Science solution which aims to support such activities. Recent work applying provenance techniques to laboratory records in chemistry is reviewed, leading to a discussion of requirements for an equivalent infrastructure to support evidence bases in social science. Progress towards the development of such a provenance architecture is then described. 1.

Abstract. The scientific model development process is often documented in an ad-hoc unstructured manner leading to difficulty in attributing provenance to data products. This can cause issues when the data owner or other interested stakeholder seeks to interpret the data at a later date. In this paper we discuss the design, development and evaluation of a Semantically-enhanced Electronic Lab-Notebook to facilitate the capture of provenance for the model development process, within the atmospheric chemistry community. We then proceed to consider the value of semantically enhanced provenance within the wider community processes, Semantically-enhanced Model-Experiment Evaluation Processes (SeMEEPs), that leverage data generated by experiments and computational models to conduct evaluations.

Abstract—In this paper, we describe how a semantic webbased provenance Interlingua called the Proof Markup Language (PML) has been used to encode workflow provenance in a variety of diverse application areas. We highlight some usability and interoperability challenges that arose in the application areas and show how PML was used in the solutions. I.

Abstract. Scientific and business communities present unprecedented requirements on provenance, where the provenance of some data item is the process that led to that data item. Previous work has conceived a computer-based representation of past executions for determining provenance, termed process documentation, and has developed a protocol, PReP, to record process documentation in service oriented architectures. However, PReP assumes a failure free environment. Failures lead to process documentation unable to be recorded, losing the evidence that a process occurred. This is not acceptable in the applications relying on process documentation and would cause disastrous consequences. This paper describes our solution, F PReP, a protocol for recording process documentation in the presence of failures. A complete formalisation of the protocol using Abstract State Machines is also presented. 1

Abstract. The concept of provenance is already well understood in the study of fine art where it refers to the trusted, documented history of some work of art. Given that documented history, the object attains an authority that allows scholars to understand and appreciate its importance and context relative to other works of art. This same concept of provenance may also be applied to data and information generated within a computer system; particularly when the information is subject to regulatory control over an extended period of time. Today’s distributed architectures (not only Agent technologies, but also Web Services ’ and GRID architectures) suffer from limitations, such as lack of mechanisms to trace results. Provenance enables users to trace how a particular result has been arrived at by identifying the individual and aggregated services that produced a particular output. In this chapter we present the main results of the EU PROVENANCE project and how these can be valuable in agent-mediated healthcare applications. For the latter we describe the Organ Transplant Management Application (OTMA), one of the demonstrator applications developed.