The Foundations for Provenance on the Web
Foundations and Trends in Web Science
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With advances in scientific instrumentation and simulation, scientific data is growing fast in both size and analysis complexity. So-called Data Grids aim to provide high performance, distributed data analysis infrastructure for data-intensive sciences, where scientists distributed worldwide need to extract information from large collections of data, and to share both data products and the resources needed to produce and store them.However, the description, composition, and execution of even logically simple scientific workflows are often complicated by the need to deal with "messy" issues like heterogeneous storage formats and ad-hoc file system structures. We show how these difficulties can be overcome via a typed workflow notation called virtual data language, within which issues of physical representation are cleanly separated from logical typing, and by the implementation of this notation within the context of a powerful virtual data system that supports distributed execution. The resulting language and system are capable of expressing complex workflows in a simple compact form, enacting those workflows in distributed environments, monitoring and recording the execution processes, and tracing the derivation history of data products.We describe the motivation, design, implementation, and evaluation of the virtual data language and system, and the application of the virtual data paradigm in various science disciplines, including astronomy, cognitive neuroscience.