Scientific Process Automation and Workflow Management - Scientific Data Management - page 468

13.5 Workflow Provenance and Execution Monitoring ................... 489

13.5.1 Example Implementation of a Provenance Framework ...... 491

13.6 Workflow Sharing and myExperiment ............................... 495

13.6.1 Workflow Reuse ............................................... 496

13.6.2 Social Sharing ................................................. 496

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13.6.3 Realizing myExperiment 497

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13.7 Conclusions and Future Work 499

Acknowledgments .......................................................... 500

References ................................................................. 501

13.1 Introduction
Scientific discoveries in the natural sciences are increasingly data driven and
computationally intensive, providing unprecedented data analysis and scien-
tific simulation opportunities. To accelerate scientific discovery through ad-
vanced computing and information technology, various research programs
have been launched in recent years, for example, the SciDAC program by
the Department of Energy 1 and the Cyberinfrastructure initiative by the Na-
tional Science Foundation, 2 both in the United States. In the UK, the term
e-Science 3 was coined to describe computationally and data-intensive science,
and a large e-Science research program was started there in 2000. With the
new opportunities for scientists also come new challenges, for example, man-
aging the enormous amounts of data generated 4 and the increasingly sophis-
ticated but also more complex computing environments provided by cluster
computers and distributed grid environments. Scientific workflows aim to ad-
dress many of these challenges.
In general terms, a scientific workflow is a formal description of a process
for accomplishing a scientific objective, usually expressed in terms of tasks
and their dependencies. 5 - 7 Scientific workflows can be used during several
different phases of a larger science process, that is, the cycle of hypothesis for-
mation, experiment design, execution, and data analysis. 8 , 86 Scientific work-
flows can include steps for the acquisition, integration, reduction, analysis,
visualization, and publication (for example, in a shared database) of scientific
data. Similar to more conventional business workflows, 9 scientific workflows
are composed of individual tasks that are organized at workflow design time
and whose execution is orchestrated at runtime according to dataflow and
task dependencies as specified by the workflow designer. Workflows are of-
ten designed visually, for example, using block diagrams, or textually, using a
domain-specific language. From a scientist's perspective, scientific workflows
constitute knowledge artifacts or “recipes” that provide a means to automate,
document, and make repeatable a scientific process.