616 research outputs found

    Secure abstraction views for scientific workflow provenance querying

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    The lifecycle of provenance metadata and its associated challenges and opportunities

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    This chapter outlines some of the challenges and opportunities associated with adopting provenance principles and standards in a variety of disciplines, including data publication and reuse, and information sciences

    Labeling Workflow Views with Fine-Grained Dependencies

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    This paper considers the problem of efficiently answering reachability queries over views of provenance graphs, derived from executions of workflows that may include recursion. Such views include composite modules and model fine-grained dependencies between module inputs and outputs. A novel view-adaptive dynamic labeling scheme is developed for efficient query evaluation, in which view specifications are labeled statically (i.e. as they are created) and data items are labeled dynamically as they are produced during a workflow execution. Although the combination of fine-grained dependencies and recursive workflows entail, in general, long (linear-size) data labels, we show that for a large natural class of workflows and views, labels are compact (logarithmic-size) and reachability queries can be evaluated in constant time. Experimental results demonstrate the benefit of this approach over the state-of-the-art technique when applied for labeling multiple views.Comment: VLDB201

    Towards Exascale Scientific Metadata Management

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    Advances in technology and computing hardware are enabling scientists from all areas of science to produce massive amounts of data using large-scale simulations or observational facilities. In this era of data deluge, effective coordination between the data production and the analysis phases hinges on the availability of metadata that describe the scientific datasets. Existing workflow engines have been capturing a limited form of metadata to provide provenance information about the identity and lineage of the data. However, much of the data produced by simulations, experiments, and analyses still need to be annotated manually in an ad hoc manner by domain scientists. Systematic and transparent acquisition of rich metadata becomes a crucial prerequisite to sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and domain-agnostic metadata management infrastructure that can meet the demands of extreme-scale science is notable by its absence. To address this gap in scientific data management research and practice, we present our vision for an integrated approach that (1) automatically captures and manipulates information-rich metadata while the data is being produced or analyzed and (2) stores metadata within each dataset to permeate metadata-oblivious processes and to query metadata through established and standardized data access interfaces. We motivate the need for the proposed integrated approach using applications from plasma physics, climate modeling and neuroscience, and then discuss research challenges and possible solutions

    Scalable And Secure Provenance Querying For Scientific Workflows And Its Application In Autism Study

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    In the era of big data, scientific workflows have become essential to automate scientific experiments and guarantee repeatability. As both data and workflow increase in their scale, requirements for having a data lineage management system commensurate with the complexity of the workflow also become necessary, calling for new scalable storage, query, and analytics infrastructure. This system that manages and preserves the derivation history and morphosis of data, known as provenance system, is essential for maintaining quality and trustworthiness of data products and ensuring reproducibility of scientific discoveries. With a flurry of research and increased adoption of scientific workflows in processing sensitive data, i.e., health and medication domain, securing information flow and instrumenting access privileges in the system have become a fundamental precursor to deploying large-scale scientific workflows. That has become more important now since today team of scientists around the world can collaborate on experiments using globally distributed sensitive data sources. Hence, it has become imperative to augment scientific workflow systems as well as the underlying provenance management systems with data security protocols. Provenance systems, void of data security protocol, are susceptible to vulnerability. In this dissertation research, we delineate how scientific workflows can improve therapeutic practices in autism spectrum disorders. The data-intensive computation inherent in these workflows and sensitive nature of the data, necessitate support for scalable, parallel and robust provenance queries and secured view of data. With that in perspective, we propose OPQLPigOPQL^{Pig}, a parallel, robust, reliable and scalable provenance query language and introduce the concept of access privilege inheritance in the provenance systems. We characterize desirable properties of role-based access control protocol in scientific workflows and demonstrate how the qualities are integrated into the workflow provenance systems as well. Finally, we describe how these concepts fit within the DATAVIEW workflow management system

    Metadata and provenance management

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    Scientists today collect, analyze, and generate TeraBytes and PetaBytes of data. These data are often shared and further processed and analyzed among collaborators. In order to facilitate sharing and data interpretations, data need to carry with it metadata about how the data was collected or generated, and provenance information about how the data was processed. This chapter describes metadata and provenance in the context of the data lifecycle. It also gives an overview of the approaches to metadata and provenance management, followed by examples of how applications use metadata and provenance in their scientific processes

    Querying and managing opm-compliant scientific workflow provenance

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    Provenance, the metadata that records the derivation history of scientific results, is important in scientific workflows to interpret, validate, and analyze the result of scientific computing. Recently, to promote and facilitate interoperability among heterogeneous provenance systems, the Open Provenance Model (OPM) has been proposed and has played an important role in the community. In this dissertation, to efficiently query and manage OPM-compliant provenance, we first propose a provenance collection framework that collects both prospective provenance, which captures an abstract workflow specification as a recipe for future data derivation and retrospective provenance, which captures past workflow execution and data derivation information. We then propose a relational database-based provenance system, called OPMPROV that stores, reasons, and queries prospective and retrospective provenance, which is OPM-compliant provenance. We finally propose OPQL, an OPM-level provenance query language, that is directly defined over the OPM model. An OPQL query takes an OPM graph as input and produces an OPM graph as output; therefore, OPQL queries are not tightly coupled to the underlying provenance storage strategies. Our provenance store, provenance collection framework, and provenance query language feature the native support of the OPM model
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