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From the World Wide Web to supply chains and scientific simulations, distributed systems are a widely used and important approach to building computational systems. Tracking provenance within these systems is crucial for determining the trustworthiness of data they produce, troubleshooting problems, assigning responsibility for decisions, and improving performance. To facilitate such tracking, the Open Provenance Model (OPM) has been created to enable the interchange of provenance between a distributed system's components. However, to date, the ability of OPM to represent distributed systems has not been verified. In this work, we show how OPM can be used to represent a set of distributed systems' patterns. We present a profile that shows that these patterns are a specialisation of OPM. Finally, we define a contract that enables participants in a distributed system to ensure that their provenance can be integrated cohesively. © 2011 Elsevier B.V. All rights reserved.

The Open Provenance Model is a model of provenance that is designed to meet the following requirements: (1) Allow provenance information to be exchanged between systems, by means of a compatibility layer based on a shared provenance model. (2) Allow developers to build and share tools that operate on such a provenance model. (3) Define provenance in a precise, technology-agnostic manner. (4) Support a digital representation of provenance for any "thing", whether produced by computer systems or not. (5) Allow multiple levels of description to coexist. (6) Define a core set of rules that identify the valid inferences that can be made on provenance representation. This document contains the specification of the Open Provenance Model (v1.1) resulting from a community effort to achieve inter-operability in the Provenance Challenge series. © 2011 Elsevier B.V. All rights reserved.

This paper presents the provenance storing system prOOst which uses a semi-structured approach to store the provenance data based on the Open Provenance Model (OPM). It uses the graph database "Neo4j" for storage and the graph traversal language "Gremlin" for querying. Furthermore, it provides a REST interface to record data into the store, and a web front end to query the database. The prOOst provenance system was published as Open Source software and is available on SourceForge.

Trust for the data created and processed on e-Science environment can be estimated with provenance. The information to form provenance, which says how the data was created and reached its current state, increases as data evolves. It is a heavy burden to trace and verify the massive provenance along the history of data in order to trust data. On the other hand, it is another issue how to trust the verification of data with provenance assuming that the provenance is believable. This paper proposes the property-based trust reasoning which cuts down the overhead to track the history and the origin of data with provenance by semantic path on Open Provenance Model(OPM). Also, the domain-based trust reasoning is adopted, which uses the domain specialty of e-Science environment. The two trust reasonings form the multi-layer trust reasoning. The effectiveness of the proposal is shown by quantitative analysis of overhead reduction as well as by qualitative analysis of trust reasoning.

Global climate researchers rely upon many forms of sensor data and analytical methods to help profile subtle changes in climate conditions. The U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) program provides researchers with a collection of curated Value Added Products (VAPs) resulting from continuous sensor data streams, data fusion, and modeling. We are leveraging the Open Provenance Model as a foundational construct that serves the needs of both the VAP producers and consumers. We are organizing the provenance in different tiers of granularity to model VAP lineage, causality at the component level within a VAP, and the causality for each time step as samples are being assembled within the VAP. This paper shares our implementation strategy and how the ARM operations staff and the climate research community can greatly benefit from this approach to more effectively assess and quantify VAP provenance.

Sensor web applications such as real-time environmental decision support systems require the use of sensors from multiple heterogeneous sources for purposes beyond the scope of the original sensor design and deployment. In such cyberenvironments, provenance plays a critical role as it enables users to understand, verify, reproduce, and ascertain the quality of derived data products. Such capabilities are yet to be developed in many sensor web enablement (SWE) applications. This paper develops a provenance-aware “Virtual Sensor” system, where a new persistent live “virtual” sensor is re-published in realtime after some model-based computational transformations of the raw sensor data streams. We describe the underlying OPM (Open Provenance Model) API's (Application Programming Interfaces), architecture for provenance capture, creation of the provenance graph and publishing of the provenance-aware virtual sensor where the new virtual sensor time-series data is augmented with OPM-compliant provenance information. A case study on creating real-time provenance-aware virtual rainfall sensors is illustrated. Such a provenance-aware virtual sensor system allows digital preservation and verification of the new virtual sensors.

In this paper, we introduce the Open Provenance Model, a model for provenance that is designed to meet the following requirements: (1) To allow provenance information to be exchanged between systems, by means of a compatibility layer based on a shared provenance model. (2) To allow developers to build and share tools that operate on such a provenance model. (3) To define the model in a precise, technology-agnostic manner. (4) To support a digital representation of provenance for any "thing", whether produced by computer systems or not. (5) To define a core set of rules that identify the valid inferences that can be made on provenance graphs.

The Open Provenance Model (OPM) has recently been proposed as an exchange framework for workflow provenance information. In this paper we show how the NRC data model for workflow repositories can be mapped to the OPM . Our mapping includes such features as complex data flow in an execution of a workflow; different workflows in the repository that call each other; and the tracking of subvalues of complex data structures in the provenance information. Because the NRC dataflow model has been formally specified, also our mapping can be formally specified; in particular, it can be automated. To facilitate this specification, we present an adapted set-theoretic formalization of the basic OPM .

Excerpt from the introduction: ... A conceptual data model for dataflow repositories should offer a precise specification of the types of data (including the data flows themselves) stored in the repository, and of the relationships among them. Such a data model is important because it provides a formal framework that allows: • Analyzing, in a rigorous manner, the possibilities and limitations of dataflow repositories. • Comparing, again in a rigorous manner, the functionality of different existing systems. • Highlighting differences in meaning of common notions as used by different authors or in different systems, such as "workflow" , "provenance" , or "collection" . It should be clear that the purpose of our contribution is not to propose a blueprint for a new dataflow management system with innovative features that "competing" systems do not support yet. Rather, our work is a detailed effort to model such a system, hoping to contribute a formally defined synthesis of some key database aspects of dataflow management systems. Indeed, each of the aspects we touch upon has been addressed in existing systems, of course each particular system with its own emphasis. In a perfect world, one would like a standard database schema for the exchange of dataflow specifications and executions, similar to the myExperiment.org initiative that is specific to the Taverna system. The need for such a repository has also been emphasized by Lacroix. Our work is a new step in this direction, after initial steps during the 1990s did not receive the follow-up they deserved. The need for a workflow repository is also acknowledged in other fields, as shown by Blockeel and Vanschoren's Experiment Databases for Machine Learning. Dataflow management systems have been largely developed within the computer systems community, and have received less interest in the database theory community. We hope to partially fill this gap by the present work. We do note that much attention has been paid to automated verification of data intensive workflows, but this is a research focus that is orthogonal to the focus on data modelling and querying taken in this work. For a review of scientific workflow management and provenance systems, we refer to Freire et al., Yogesh et al., Bose and Frew, and Davidson and Freire. Where-provenance is one form of data provenance as investigated in database research. In this work we show how the concept of where-provenance can also be defined in the context of workflow provenance. ...

DEVS models are getting to be more and more sophisticated due to large scale objectives such as modeling complex physical continuous systems. The assurance of the validity and quality of simulation data handling and analysis may lead to a great challenge. Thus, a need of providing insight of DEVS simulation performance can arise. In particular, information about simulation processes workflow and internal data values and management. Provenance-aware systems are designed to satisfy those needs by documenting processes and casual dependencies. Provenance is a critical concept in scientific workflows, since it allows scientists to understand the origin of their results, to repeat their experiments, and to validate the processes that were used to derive data products. Recently, the provenance community agreed that a "Provenance Challenge" should be set to compare and understand existing approaches and updated the Open Provenance Model (OPM). We introduce provenance documentation technology to uncover DEVS models behaviours during a simulation. In addition, we apply the OPM formalization due to interoperability reasons and provide support to the Third Provenance Challenge.

Provenance is well understood in the context of art or digital libaries, where it respectively refers to the documented history of an art object, or the documentation of processes in a digital object's life cycle. Interest for provenance in the "e-science community" [12] is also growing, since provenance is perceived as a crucial component of workflow systems that can help scientists ensure reproducibility of their scientific analyses and processes [2,4].