Provenance Services for Distributed Workflows

Cruz, Sérgio Manuel Serra da; Barros, Patricia; Bisch, Paulo Mascarello; Campos, María Luiza Machado; Mattoso, Marta

doi:10.1109/ccgrid.2008.73

Cited by 15 publications

(7 citation statements)

References 22 publications

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“…The difficulty arises in provenance recording when workflows are executed in distributed differentcontrol domains. Cruz et al [197] explores this characteristic, presenting a Matrioshka architecture to globally keep track of provenance. However, more fine-grained collaboration is needed for the provenance format conformity.…”

Section: J Trust In Provenance-based Service Workflowsmentioning

confidence: 99%

A Survey of Trust in Workflows and Relevant Contexts

Viriyasitavat

Martin

2011

IEEE Commun. Surv. Tutorials

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With the fast-growing Internet technology, several service-based interactions are prevalent and appear in several forms such as e-commerce, content provider, Virtual Organizations, Peer-to-Peer, Web Service, Grids, Cloud Computing, and individual interactions. This demands for an effective mechanism to establish trust among participants in a highlevel abstract way, capturing relevant factors ranging on Service Level Agreement, security policies, requirements, regulations, constraints, Quality of Service, reputation, and recommendation. Trust is platform-independent and flexible to be seamlessly integrated into heterogeneous domains and interoperate with different security solutions in distributed environments. Establishing trust in a service workflow leads to the willingness of services to participate. Coordinating service workflows without trust consideration may pose higher risks, possibly results in poor performance, additional vulnerabilities, or failures. Although trust in service workflows and relevant contexts has been studied for a past decade, the standard development is still immature. Nowadays, trust approaches to service workflows comprise a large area of research where one can hardly classify into a comprehensive survey. This survey examines and explores the role of trust in service workflows and their contexts from a wide variety of literatures. Various mechanisms, architecture, techniques, standards, and frameworks are explained along the way with discussions. Working trust definition and classification are newly provided and supported with examples.

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Section: J Trust In Provenance-based Service Workflowsmentioning

confidence: 99%

A Survey of Trust in Workflows and Relevant Contexts

Viriyasitavat

Martin

2011

IEEE Commun. Surv. Tutorials

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“…Meanwhile, the Qurator system [24,25] takes a step into breaking down black boxes by splitting them into individual iterations over data items instead of operating on collections. Reference [13] also puts explicit control loops in WfMS to get finer grained information about process execution. Unfortunately, these systems do not yet look at how data item attributes affect process execution.…”

Section: Related Workmentioning

confidence: 99%

Understanding provenance black boxes

Chapman

Jagadish

2010

Distrib Parallel Databases

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Current provenance stores associated with workflow management systems (WfMSs) capture enough coarse-grained information to describe which datasets were used and which processes were run. While this information is enough to rebuild a workflow run, it is not enough to facilitate user understanding. Because the data is manipulated via a series of black boxes, it is often impossible for a human to understand what happened to the data. In this work, we highlight the missing information that can assist user understanding. Unfortunately, provenance information is already very complex and difficult for a user to comprehend, which can be exacerbated by adding the extra information needed for deeper blackbox understanding. In order to alleviate this, we develop a model of provenance answers that follow a "roll up", "drill down" strategy. We evaluate these techniques to determine if users have better understanding of provenance information. We show how this information can be captured by workflow management systems, and that the structures and information needed for this model are a negligible addition to standard provenance stores. Finally, we implement these techniques in a real provenance system, and evaluate implementation feasibility.

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“…Several works share the idea of a provenance management system that is independent of a workflow system technology. However, the main difficulty of being SWfMS‐agnostic is that the SWfMS and the provenance management system need to communicate to exchange information.…”

Section: Introductionmentioning

confidence: 99%

ProvManager: a provenance management system for scientific workflows

Marinho

Murta

Werner

et al. 2011

Concurrency and Computation

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Running scientific workflows in distributed and heterogeneous environments has been a motivating approach for provenance management, which is loosely coupled to the workflow execution engine. This kind of approach is interesting because it allows both storage and access to provenance data in a homogeneous way, even in an environment where different workflow management systems work together. However, current approaches overload scientists with many ad hoc tasks, such as script adaptations and implementations of extra functionalities to provide provenance independence. This paper proposes ProvManager, a provenance management approach that eases the gathering, storage, and analysis of provenance information in a distributed and heterogeneous environment scenario, without putting the burden of adaptations on the scientist. ProvManager leverages the provenance management at the experiment level by integrating different workflow executions from multiple workflow management systems.

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Provenance Services for Distributed Workflows

Cited by 15 publications

References 22 publications

A Survey of Trust in Workflows and Relevant Contexts

A Survey of Trust in Workflows and Relevant Contexts

Understanding provenance black boxes

ProvManager: a provenance management system for scientific workflows

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