One of the greatest challenges in computational chemistry is the design of enzymes to catalyze non-natural chemical reactions. We focus on harnessing the distributed parallel comp...
Jianwu Wang, Prakashan Korambath, Seonah Kim, Scot...
MapReduce has recently gained a lot of attention as a parallel programming model for scalable data-intensive business and scientific analysis. In order to benefit from this powerf...
In this paper we propose to achieve a semantic equivalence between a visual- and a script-based workflow development paradigm. We accomplish this by building a script language whi...
Scientific workflows facilitate automation, reuse, and reproducibility of scientific data management and analysis tasks. Scientific workflows are often modeled as dataflow networks...
Steps in scientific workflows often generate collections of results, causing the data flowing through workflows to become increasingly nested. Because conventional workflow compone...
Timothy M. McPhillips, Shawn Bowers, Bertram Lud&a...
Scientific workflows have gained great momentum in recent years due to their critical roles in e-Science and cyberinfrastructure applications. However, some tasks of a scientific w...
Liqiang Wang, Shiyong Lu, Xubo Fei, Jeffrey L. Ram
Grid workflows can be seen as special scientific workflows involving high performance and/or high throughput computational tasks. Much work in grid workflows has focused on improvi...
Ilkay Altintas, Adam Birnbaum, Kim Baldridge, Wibk...
Species distribution prediction modeling plays a key role in biodiversity research. We propose to publish both species distribution data and modeling components as Web services and...
Jianting Zhang, Deana Pennington, William Michener
The tools used to analyze scientific data are often distinct from those used to archive, retrieve, and query data. A scientific workflow environment, however, allows one to seamles...
Chad Berkley, Shawn Bowers, Matthew B. Jones, Bert...