Scientific Workflow, Provenance, and Data Modeling Challenges and Approaches
نویسندگان
چکیده
منابع مشابه
Approaches for Exploring and Querying Scientific Workflow Provenance Graphs
While many scientific workflow systems track and record data provenance, few tools have been developed that provide convenient and effective ways to access and explore this information. Two important ways for provenance information to be accessed and explored is through browsing (i.e., visualizing and navigating data and process dependencies) and querying (e.g., to select certain portions of pr...
متن کاملLabelFlow: Exploiting Workflow Provenance to Surface Scientific Data Provenance
Provenance traces captured by scientific workflows can be useful for designing, debugging and maintenance. However, our experience suggests that they are of limited use for reporting results, in part because traces do not comprise domain-specific annotations needed for explaining results, and the black-box nature of some workflow activities. We show that by basic mark-up of the data processing ...
متن کاملProvenance in Scientific Workflow Systems
The automated tracking and storage of provenance information promises to be a major advantage of scientific workflow systems. We discuss issues related to data and workflow provenance, and present techniques for focusing user attention on meaningful provenance through “user views,” for managing the provenance of nested scientific data, and for using information about the evolution of a workflow...
متن کاملEvaluating Workflow Trust using Hidden Markov Modeling and Provenance Data
In service-oriented environments, services provide different qualities in terms of parameters like availability, cost, reputation, execution time, etc. A trust score can be derived from these QoS parameters, which determines the rate of reliability in each service. This score can assist the service consumer parties to decide whether or not to transact with that service provider in the future. I...
متن کاملThe PBase Scientific Workflow Provenance Repository
Scientific workflows and their supporting systems are becoming increasingly popular for compute-intensive and data-intensive scientific experiments. The advantages scientific workflows offer include rapid and easy workflow design, software and data reuse, scalable execution, sharing and collaboration, and other advantages that altogether facilitate “reproducible science”. In this context, prove...
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ژورنال
عنوان ژورنال: Journal on Data Semantics
سال: 2012
ISSN: 1861-2032,1861-2040
DOI: 10.1007/s13740-012-0004-y