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Mendeley readers
Attention Score in Context
| Title |
A comparison of using Taverna and BPEL in building scientific workflows: the case of caGrid
|
|---|---|
| Published in |
Concurrency & Computation: Practice & Experience, November 2009
|
| DOI | 10.1002/cpe.1547 |
| Pubmed ID | |
| Authors |
Wei Tan, Paolo Missier, Ian Foster, Ravi Madduri, David De Roure, Carole Goble |
| Abstract |
With the emergence of "service oriented science," the need arises to orchestrate multiple services to facilitate scientific investigation-that is, to create "science workflows." We present here our findings in providing a workflow solution for the caGrid service-based grid infrastructure. We choose BPEL and Taverna as candidates, and compare their usability in the lifecycle of a scientific workflow, including workflow composition, execution, and result analysis. Our experience shows that BPEL as an imperative language offers a comprehensive set of modeling primitives for workflows of all flavors; while Taverna offers a dataflow model and a more compact set of primitives that facilitates dataflow modeling and pipelined execution. We hope that this comparison study not only helps researchers select a language or tool that meets their specific needs, but also offers some insight on how a workflow language and tool can fulfill the requirement of the scientific community. |
Mendeley readers
The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 5 | 9% |
| United States | 1 | 2% |
| Greece | 1 | 2% |
| Canada | 1 | 2% |
| Unknown | 45 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 25% |
| Professor | 8 | 15% |
| Student > Ph. D. Student | 8 | 15% |
| Student > Master | 7 | 13% |
| Student > Doctoral Student | 4 | 8% |
| Other | 9 | 17% |
| Unknown | 4 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 28 | 53% |
| Agricultural and Biological Sciences | 5 | 9% |
| Engineering | 4 | 8% |
| Medicine and Dentistry | 3 | 6% |
| Biochemistry, Genetics and Molecular Biology | 2 | 4% |
| Other | 5 | 9% |
| Unknown | 6 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 July 2010.
All research outputs
#5,398,346
of 25,373,627 outputs
Outputs from Concurrency & Computation: Practice & Experience
#39
of 528 outputs
Outputs of similar age
#28,623
of 176,926 outputs
Outputs of similar age from Concurrency & Computation: Practice & Experience
#1
of 4 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 528 research outputs from this source. They receive a mean Attention Score of 2.9. This one has done particularly well, scoring higher than 92% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 176,926 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them