Difference between revisions of "Integrate SPREAD algorithms in Statistical Manager"
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* The activity consisted in adding the two algorithms (both R script & wrapping Java class) to the [https://issue.i-marine.research-infrastructures.eu/browser/private/emmanuel.blondel/statistical-manager-figis-algorithms statistical-manager-figis-algorithms] project that hosts FAO experiments, along with performing tests. | * The activity consisted in adding the two algorithms (both R script & wrapping Java class) to the [https://issue.i-marine.research-infrastructures.eu/browser/private/emmanuel.blondel/statistical-manager-figis-algorithms statistical-manager-figis-algorithms] project that hosts FAO experiments, along with performing tests. | ||
* The updated archives and R scripts where shared with the Statistical Manager team | * The updated archives and R scripts where shared with the Statistical Manager team | ||
| − | * The | + | * The Statistical Manager team deployed the algorithms in the [https://dev3.d4science.org/group/devvre/sm iMarine development portal] |
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== Conclusion == | == Conclusion == | ||
Revision as of 12:18, 20 June 2014
Hypothesis and Thesis
Ongoing experiment - under editing
This experiment is performed by FAO in order to further test and assess how data managers / developers can plug easily algorithms (especially R algorithms) in the infrastructure, through the Statistical Manager tool, and respond quickly to data analysis needs while benefiting of iMarine computing resources.
The product of this experiment include two Spatial Data Reallocation (SPREAD) algorithms:
- one generic, with more parameters
- one simplified, in order to better adjust SPREAD needs of the FAO Fisheries & Aquaculture department
The scope of these algorithm integration experiments is:
- developer/algorithm integrator oriented
- to assess how a data manager / developer can plug an algorithm by their own,
- to identify potential improvements for the ease, speed and sustainability of the R algorithm integration procedure
- end-user oriented
- to assess user friendliness of the Statistical Manager data analysis tool
Outcome
The results of this experiment confirm the results of the first experiment (SDMX Data converter) and show that the procedure of integrating R scripts as data analysis algorithms is a quick, straightforward and sustainable.
In addition to the outcome of the first experiment, the present experiment highlighted the flexibility of the Statistical Manager and its capacity to simplify algorithms inputs to guarantee user-friendliness of the algorithm execution by the end-user.
Activity Workflow
- The activity consisted in adding the two algorithms (both R script & wrapping Java class) to the statistical-manager-figis-algorithms project that hosts FAO experiments, along with performing tests.
- The updated archives and R scripts where shared with the Statistical Manager team
- The Statistical Manager team deployed the algorithms in the iMarine development portal
Conclusion
TBD
Recommendations & future developments
TBD
Experimentation
TBD
Related links
- How to implement algorithms for the Statistical Manager
- Implement Statistical Manager algorithms - tutorial video
- rsdmx package for R used for the reading SDMX-ML catch data in R
- RFigisGeo package for R used for reading intersections & performing the spatial data reallocation
