期刊名称：Bulletin of the Technical Committee on Data Engineering
出版年度：2010
卷号：33
期号：03
出版社：IEEE Computer Society
摘要：eScience has established itself as a key pillar in scientific discovery, continuing the evolution of the scientific
discovery process from theoretical to empirical to computational science [13]. Extensive deployment of instru-
ments and sensors that observe the physical and biological world are bringing in large and diverse data to the
reach of scientists. Often, that data is more frequently shared due to the cost of the instrumentation or because
of the desire to address larger scale and/or cross-discipline science such as climate change. There is a tangible
push towards building large, global-scale instruments [2][3] and wide deployment of sensors [1] with the data
they generate being shared by a large collaboration which has access to the data generated by these instruments.
Indeed, funding agencies and publishers are starting to insist that scientists share both results and raw datasets,
along with the provenance for how the result was produced from the raw dataset(s), to foster open science [4].
Scientific workflows have emerged as the de facto model for researchers to process, transform and analyze
scientific data. These workflows may run on the users desktop or in the Cloud and the workflow framework is
geared towards easy composition of scientific experiments, allocation and scheduling of resources, orchestration
and monitoring of execution, and collecting provenance [20]. The goal of the Trident Scientific Workflow
System is to provide a specialized programming environment to simplify the programming effort required by
scientists to orchestrate a computational science experiment.
