In computational statistics, algorithms often have specialized implementations that address very specific problems. Every so often, these algorithms are applicable also to other problems than the original ones. Today, interest is growing towards modular and pluggable solutions that enable the repetition and validation of the experiments made by other scientists and allow the exploitation of those algorithms in other contexts. Furthermore, such procedures are requested to be remotely hosted and to “hide” the complexity of the calculations, managed by remote computational infrastructures behind the scenes. For such reasons, the usual solution of supplying modular software libraries containing implementations of algorithms is leaving the place to Web Services accessible through standard protocols and hosting such implementations. The protocols describing the computational capabilities of these Services are more and more elaborate, so that modular workflows can rely on them.

1. Providing Statistical Algorithms
as-a-Service
Gianpaolo Coro, Pasquale Pagano,
Leonardo Candela
ISTI-CNR, Pisa, Italy

2. Statistical Manager
Statistical Manager is a set of web services that aim to:
• Help scientists in managing marine, biological or climatic statistical problems
• Supply precooked state-of-the-art algorithms as-a-Service
• Perform calculations by using Cloud computing in a transparent way to the users
• Share input, results, parameters and comments with colleagues by means of Virtual
Research Environment in the D4Science e-Infrastructure
Setup and execution
Statistical
Manager
Sharing
D4Science
Computational
Facilities

3. Architecture

4. Internal Work

5. Resources and Sharing

6. Statistical Manager - Interface

7. Experiment Execution

8. Computations Check
Summary of the Input, Output
and Parameters of the experiment

9. Data Space - Sharing and Import

10. Hosted Algorithms

11. Application Fields
o
o
o
o
Ecology
Environment
Biodiversity
Life

12. Ecology

13. Niche Modelling
•
•
•
•
•
AquaMaps – Suitable Habitat
AquaMaps – Native Habitat
AquaMaps for 2050
Artificial Neural Networks
AquaMaps - ANN
Gadus morhua
AquaMaps - Suitable Habitat

14. Outliers Detection
Presence
Points
Cetorhinus maximus
Density-based
Clustering
and Outliers detection
Distance Based Clustering
DBScan
K-Means
X-Means

15. Climate Changes Effects on Species
Bioclimate HSpec
Overall occupancy in
time
Estimated impact of climate
changes over 20 years on 11549
species.
Pseudanthias evansi
The occupancy by the
Pseudanthias evansi
decreases in Area 71 but
increases in Area 77

16. Similarity between habitats
Habitat Representativeness Score:
1.
Measures the similarity between the environmental features of two areas
2. Assesses the quality of models and environmental features
Latimeria chalumnae
HRS=10.5
Habitat
Representativeness
Score

17. Environment

18. Rasterization
A polygonal map is
transformed into a raster
map or into a point map

19. Maps Comparison
compare
Compares :
• Species Distribution
maps
• Environmental layers
• SAR Images

20. Periodicity and Seasonality
Periodicity: 12 months
Extraction Tools
Fourier Analysis

21. Environmental Signal Processing
Spectrogram
Resampling

22. Biodiversity

23. Occurrence Points
Occurrence Data from GBIF
Occurrence Data from Obis
∩
ᴜ
-
Intersection
Union
Difference
DD
Duplicates Deletion
A
B
x,y
x,y
Event Date
Records
Modif Date
Modif Date
Author
Species Scientific Name
Event Date
Similarity
Author
Species Scientific Name

24. BiOnym
Raw Input String.
E.g. Gadus morua Lineus 1758
Reference
Source
(ASFIS)
Preprocessing
And
Parsing
Accounts for:
• Variations in the spelling and
interpretation of taxonomic
names
• Combination of data from
different sources
• Harmonization and reconciliation
of Taxa names
Reference
Source
(Other in
DwC-A)
Reference
Source
(WoRMS)
Taxon name
Matcher 1
A flexible workflow approach to
taxon name matching
Reference
Source
(FISHBASE)
Taxon name
Matcher 2
Taxon name
Matcher n
PostProcessing
Correct Transcriptions:
E.g. Gadus morhua (Linnaeus, 1758)

25. Trendylyzer
• Fill some knowledge gaps on marine species
• Account for sampling biases
• Define trends for common species
Herring recovered after the fish ban
Plankton regime shift
Can we recognize big changes in
species presence?

26. Life

27. Length-Weight Relationships
Calculate the a and b parameters for 14 230
species by means of Bayesian Methods
Approach:
Collaborative development with the final user
Integration of user’s R Scripts
Usage of Cloud computing for R Scripts
Periodic runs
bluewatermag.com.au
The porting to the D4Science Statistical Manager allowed to run the scripts in distributed
fashion
The time reduction was from 20 days to 11 hours! 95.4% reduction

28. Functions Simulation - Spawning Stock Biomass vs Recruits
Estimate biological limits for 50
Northeast Atlantic fish stocks
Use real measures
Rely on previous expert knowledge
Use Bayesian models to combine
information
Re-estimated SSB limit
Re-estimated HS
Rulebased
HS
Re-estimated
precautionary limit

29. Future Work

30. Plan
• Make the Statistical Manager Algorithms accessible
through the OGC WPS standard (currently available via
SOAP and Java API)
• Invoke the algorithms from a Workflow Management
System (e.g. Taverna)
• Expand the system with new algorithms

31. Thank you