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Providing Statistical Algorithms as-a-Service
- 1. Providing Statistical Algorithms as-a-Service GianpaoloCoro, Pasquale Pagano, Leonardo Candela ISTI-CNR, Pisa, Italy
- 2. Statistical Manager Statistical Manageris 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
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- 6. Statistical Manager -Interface
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- 8. Computations Check Summary ofthe Input, Output and Parameters of the experiment
- 9. Data Space -Sharing and Import
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- 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 andOutliers detection Distance Based Clustering DBScan K-Means X-Means
- 15. Climate Changes Effectson 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 HabitatRepresentativeness 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
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- 18. Rasterization A polygonal mapis 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
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- 23. Occurrence Points Occurrence Datafrom 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 someknowledge 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?
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- 27. Length-Weight Relationships Calculate thea 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
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- 30. Plan • Make theStatistical 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
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