57
BlueBRIDGE receives funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 675680 www.bluebridge-vres.eu CMSY Workshop Gianpaolo Coro ISTI-CNR [email protected]

CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Embed Size (px)

Citation preview

Page 1: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

BlueBRIDGE receives funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 675680 www.bluebridge-vres.eu

CMSY Workshop

Gianpaolo Coro [email protected]

Page 2: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Verhulst (1844) Model of Population Growth

Page 3: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

The Schaefer Model (1954)Fmsy = ½ rmax

Bmsy = ½ k

Page 4: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

http://onlinelibrary.wiley.com/doi/10.1111/faf.12190/full

CMSY

An Open-source software for data-limited stock assessment

https://github.com/SISTA16/cmsy

Page 5: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

From Catch-MSY to CMSY• Catch-MSY gave robust

estimates of MSY, but biased estimates of r (too low) and k (too high).

Catch-MSY could not reliably predict biomass

CMSY overcomes the bias and gives reasonable estimates of Fmsy and Bmsy

CMSY gives reasonable estimates of biomass

Page 6: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Input

https://github.com/SISTA16/cmsy

https://github.com/SISTA16/cmsy/blob/master/CMSY_UserGuide_24Oct16.docx

Resilience prior r rangeHigh 0.6 – 1.5Medium 0.2 – 0.8Low 0.05 – 0.5Very low 0.015 – 0.1

stock NameEnglishName

ScientificName Source Resilience StartYear EndYear

Biomass status

beginning

Biomass status

end TypePossible

Crash

her-47d3

Herring in Sub-area IV,

Divisions VIId & IIIa (autumn-spawners)

Atlantic herring

Clupea harengus

www.ices.dk Medium 1947 2013 Good/Bad Good/Bad

Biomass/CPUE/none

No

Stock ID Year Catch Biomass/CPUEher-47d3 1947 581760 7053257her-47d3 1948 502100 6362933her-47d3 1949 508500 6070794her-47d3 1950 491700 6119555her-47d3 1951 600400 6199629her-47d3 1952 664400 6058665her-47d3 1953 698500 5950584her-47d3 1954 762900 5809471

… … … …

ID File:

Time Series File: Estimated status of the biomass at the beginning and the end of the time series

Page 7: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Output

Illex coindetiibroadtail shortfin squid

Analysis charts Management charts

Page 8: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

CMSY - Approach

• Given a catch trend estimate the best pair of values for the intrinsic rate of increase (r) and the carrying capacity (k) that generated the trend

• Goal: estimate r and k.

Constraint: the Schaefer function

CMSY has a double approach: Monte Carlo Analysis and Bayesian Schaefer Model

Page 9: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Step 1: sample all possible r and k pairs compliant with the Schaefer function and the priors

Step 2: resample in the lower tip. We search for the mean of maximum viable r-values

Step 3: divide the tip in 25 ranges

Step 4: take the median of the non-empty ranges

Result by CMSY analysis

True valueMonte Carlo approach

𝑀𝑆𝑌=𝑟 𝑏𝑒𝑠𝑡𝑘𝑏𝑒𝑠𝑡

4

Monte Carlo Analysis

Page 10: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Bayesian Schaefer Analysis

• In the case the Biomass or CPUE trends are available, CMSY increases the precision of the estimation:

• Goal: estimate r and k.

Constraint: the Schaefer function

Page 11: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Issues

Simple curve fitting does not work

Estimate after curve fitting

Page 12: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

1. Clustering Analysis (DBScan)

4. Viable pairs densities

3. Gaussian Mixtures2. Trapezoidal density over the best fit r-k line

Gm of the largest cluster

Simulation of r density

Search in the tip of the r-k triangle

X

Other unpromising approaches

Page 13: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Difficulty of the problem

At each step of the sampling process:

• The biomass values are strongly correlated between them

• An iterative fitting model should

• approximate the complete biomass curve using better and better r and k values

• produce a new biomass curve correlated to the previous biomass curve

• account for time dependency between the samples of one curve

Page 14: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Brain signals

Robotics

Biology

Statistics

Speech processing

Mathematics

Promising approach: Markov Chain Monte Carlo methods

Page 15: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

MCMC and the Schaefer function

𝜃={𝛼 ,𝑘 ,𝑟 ,𝑏0 ,𝑏1 ,𝑏2 , .. ,𝑏𝑇 }

b0

b1

bT…

rk𝛼

𝑏𝑡+1=𝑏𝑡−𝑐𝑡+𝒓 𝑏𝑡 (1− 𝑏𝑡

𝒌 )𝑣𝑠

• The Schaefer formula is used as likelihood(s)• Priors are required for k and r

At each step, the MCMC produces samples for these parameters: where T is the maximum time of the biomass trend

𝜃 0={𝛼 0 ,𝑘0 ,𝑟 0 ,𝑏0 0 ,𝑏1 0 ,𝑏2 0 ,.. ,𝑏𝑇 0 }

𝜃𝑀={𝛼𝑀 ,𝑘𝑀 ,𝑟𝑀 ,𝑏0 𝑀 ,𝑏1 𝑀 ,𝑏2 𝑀 ,.. ,𝑏𝑇𝑀 }𝜃 1

𝜃2

3

𝜃4

After M steps…

Hierarchical model for the variables

Details in Coro G. Gibbs Sampling with JAGS: Behind the Scenes. Technical report, 2017, CNR PUMA, cnr.isti/2017-B5-001http://puma.isti.cnr.it/dfdownload.php?ident=/cnr.isti/2017-B5-001&langver=it&scelta=Metadatahttps://www.researchgate.net/publication/313905185_Gibbs_Sampling_with_JAGS_Behind_the_Scenes

Page 16: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

• Simulating a biomass trend by means of an MCMC requires the model to produce, at each step of the sampling process, a new biomass time series by means of new values assigned to model variables

• At each step the MCMC tries to simulate the whole biomass time series using new values for r and k

• The new picked values are constrained by the Schaefer function and by the prior probability distributions that we assume for the r and k variables

• MCMC accounts for these constraints during the fitting phase. After several sampling and adjustment steps, the model finds the variables values that produce the best approximation of the target biomass trend

𝜃1={𝛼1 ,𝑘 1 ,𝑟 1 ,𝑏0 1 ,𝑏11 ,𝑏21 , .. ,𝑏𝑇 1 }

𝜃𝑀={𝛼𝑀 ,𝑘𝑀 ,𝑟𝑀 ,𝑏0 𝑀 ,𝑏1 𝑀 ,𝑏2 𝑀 ,.. ,𝑏𝑇𝑀 }….

𝜃 0={𝛼 0 ,𝑘0 ,𝑟 0 ,𝑏0 0 ,𝑏1 0 ,𝑏2 0 ,.. ,𝑏𝑇 0 }

MCMC and the Schaefer function

Page 17: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

MCMC using Gibbs Sampling

• The user takes model variables and designs a graph of the constraints between the variables

• The system writes a posterior probability density in terms of priors, likelihoods and conditionals

• The model samples variables values from each factor, using approximate or analytical forms of these factors

• At each variable sampling step, the model fixes the values of the other variables

• After several steps the values are likely to converge to the best estimate

Best estimate set

(Markov Chain)

Details in Coro G. Gibbs Sampling with JAGS: Behind the Scenes. Technical report, 2017, CNR PUMA, cnr.isti/2017-B5-001http://puma.isti.cnr.it/dfdownload.php?ident=/cnr.isti/2017-B5-001&langver=it&scelta=Metadatahttps://www.researchgate.net/publication/313905185_Gibbs_Sampling_with_JAGS_Behind_the_Scenes

Page 18: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Step 1: consider the complete r,k space. Use the CMSY points as background reference only

Step 2: produce iteratively points that are compliant with the observed Schaefer function and the priors

Step 3: concentrate the search in the accumulation area

Step 4: take the geometric mean in the accumulation area

Bayesian Schaefer Model (BSM) estimate

proxies

Page 19: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

1. Defining the form of the distributions of the priors was crucial!

This was done using 50 simulated stocks for which r and k were known

2. Defining the initial ranges of the parameters is important

This is done by the stock “expert” when indicating the prior knowledge in the ID file

3. A good balance was found between prior knowledge and knowledge from the data

This was done by testing the model for several years in Workshops and in focus groups

Key aspects of CMSY

Page 20: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

CMSY on simulated data

• CMSY was tested against 50 simulated stocks where true r, k, MSY and biomass were known

• Monte Carlo analysis included the true r-k in 100% of the cases. BSM was used as coherence check

Page 21: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

CMSY applications

ICES:WKLife IV meeting (27-31 Oct. 2014): CMSY was applied to all the data-limited stocks proposed by ICES.http://ices.dk/sites/pub/Publication%20Reports/Expert%20Group%20Report/acom/2014/WKLIFE4/wklifeIV_2014.pdf

WKLife V meeting (5-9 Oct. 2015): CMSY was applied to all the data-limited stocks proposed by ICES.http://ices.dk/sites/pub/Publication%20Reports/Expert%20Group%20Report/acom/2015/WKLIFEV/wklifeV_2015.pdf

FAO:Assessed CMSY among the best performing data-limited stocks modelshttp://www.fao.org/docrep/019/i3491e/i3491e.pdf

Is building a Web interface to produce fisheries management reports using CMSYhttp://data.d4science.org/UHZhM2pVWW1IOXRjZk9qTytQTndqaUpjamJScDg0VVVHbWJQNStIS0N6Yz0

Oceana:Based on CMSY Oceana study (on 400 stocks) found that fish catches in European waters could increase by 57% if stocks were managed sustainablyhttp://oceana.org/press-center/press-releases/oceana-study-finds-fish-catches-european-waters-could-increase-57-if

Page 22: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Results on European stocks

Page 23: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

R. Froese, C. Garilao, H. Winker, G. Coro, N. Demirel, A. Tsikliras, D. Dimarchopoulou, G. Scarcella, A. Sampang-Reyes (2016) http://eu.oceana.org/sites/default/files/stockstatusreport_newversion_0.pdf

Full Oceana report and status of EU stocks

Page 24: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

European Stocks in 2013-2015◄ Management Decision ►

Analysis of 397 stocks in European Seas and adjacent waters. Froese et al. 2016.

F &

Rep

rodu

ction

& G

row

th

Page 25: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Exploitation of 397 stocks in European Seas in 2013-2015. Note overlapping of different types of overexploitation, and therefore the numbers do not add up to 100%. Froese et al. 2016

Page 26: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Status of 397 stocks in European Seas 2013-2015. Froese et al. 2016

Page 27: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Froese et al. 2016

Compliance to Common Fisheries Policy of the European Union (CFP 2013) by Ecoregion 2013-2015

Page 28: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

1. Take the estimated biomass of the stocks in a certain region 2. Evolve the relative biomasses in time starting from values in the

neighbourhoods of B/Bmsy, F and Fmsy considering different F scenarios

3. For each evolution, cluster the B/Bmsy values and then average the values

4. Average the averages of each evolved variable, and estimate the confidence intervals

5. Plot the averaged evolutions

Producing multi-species future fisheries scenarios

𝐵𝑡+1

𝐵𝑚𝑠𝑦=

𝐵𝑡

𝐵𝑚𝑠𝑦+2𝐹𝑚𝑠𝑦

𝐵𝑡

𝐵𝑚𝑠𝑦 (1−𝐵𝑡

2𝐵𝑚𝑠𝑦 )−𝐵𝑡

𝐵𝑚𝑠𝑦𝐹

𝑡

Page 29: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Percentage of Stocks at or above Bmsy

Best rebuilding under the 0.5 Fmsy scenario, worst under the 0.95 Fmsy scenario

Rainer Froese – Presentation at the EU Parliament 27/02/2017

Page 30: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Percentage of Depleted Stocks

Best rebuilding under the 0.5 Fmsy scenario, worst under the 0.95 Fmsy scenario

Rainer Froese – Presentation at the EU Parliament 27/02/2017

Page 31: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Profitability

Good profits for the 0.5 – 0.8 Fmsy scenarios Low profit for the 0.95 Fmsy scenario

Rainer Froese – Presentation at the EU Parliament 27/02/2017

𝜋 𝑡=𝐹 𝑡

𝐹𝑚𝑠𝑦 ( 𝐵𝑡

𝐵𝑚𝑠𝑦−

(1− 𝜇𝑚𝑒𝑎𝑛

100 )( 𝐶𝑀𝑆𝑌 )

𝑚𝑒𝑎𝑛

( 𝐹𝐹𝑚𝑠𝑦 )𝑚𝑒𝑎𝑛

)

Page 32: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Analysis of current (2013 -2015) and potential catches for 397 stocks in European Seas. Because of trophic interactions, all stocks cannot support maximum yields simultaneously. Froese et al. 2016.

Page 33: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Comments on the multi-species application of CMSY (1/2)

Species interactions and environmental impact are implicitly considered in surplus production models by the rate of net productivity (r), which summarizes natural mortality such as caused by predation by other species, somatic growth such as modulated by available food sources, and recruitment such as impacted by environmental conditions and by parental egg production. CMSY accounts explicitly for reduced recruitment at small stock sizes*.

*Froese, N. Demirel, G. Coro, K. Kleisner, H. Winker, Estimating fisheries reference points from catch and resilience. Fish Fish., (in press) 10.1111/faf.12190,J.T. Schnute, L.J. Richards, “Surplus production models” in Handbook of Fish Biology and Fisheries, P.J.B. Hart, J.D. Reynolds, Eds. (Blackwell, 2002), vol. 2, pp. 105–126.T.J. Quinn, R.B. Deriso, Quantitative fish dynamics (Oxford University Press, NY, 1999)

Page 34: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Compared with age-structured models where exploitation is typically reported for a narrow range of fully selected age classes, surplus production models estimate exploitation as total catch to biomass ratio.

This is similar to using the mean exploitation rate across all age classes weighted by their respective contribution to the catch. If the catch consists to a large part of juveniles that are only partly selected by the gear, then the overall rate of fishing mortality strongly underestimates the fishing mortality of the fully selected older year classes.

In order to address the problem of underestimation of fishing mortality in fully selected age classes CMSY reduces the estimate of Fmsy as a linear function of biomass below 0.5 Bmsy.

𝐹 𝑟𝑒𝑑𝑢𝑐𝑒𝑑=2𝐵𝑡

𝐵𝑚𝑠𝑦𝐹∨

𝐵𝑡

𝐵𝑚𝑠𝑦<0.5

Comments on the multi-species application of CMSY (2/2)

Page 35: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

A collaborative approach to CMSY

Page 36: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Big Data1. Large volume

2. High generation velocity

3. Large variety

4. Untrustworthyness (veracity)

5. High complexity(variability)

Big Data: a dataset with large volume, variety, generation velocity, containing complex and untrustworthy information that requires nonconventional methods to extract, manage and process information within a reasonable time.

6. Understandable value

Page 37: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

New Science Paradigms Open Science: make scientific research, data and dissemination

accessible to all levels of an inquiring society, amateur or professional.

Keywords: Open Access, Open research, Open Notebook Science

E-Science: computationally intensive science is carried out in highly distributed network environments that use large data sets and require distributed computing and collaborative tools.

Keywords: Provenance of the scientific process, Scientific workflows

Science 2.0: process and publish large data sets using a collaborative approach. Share from raw data to experimental results and processes. Support collaborative experiments and Reproducibility-Repeatability-Reusability (R-R-R) of Science.

Keywords: collaborative and repeatable Science

Page 38: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Requirements for IT systems

• Support collaborative research and experimentation• Implement Reproducibility-Repeatability-Reusability of

Science• Allow sharing data, processes and findings• Grant free access to the produced scientific knowledge• Tackle Big Data challenges• Sustainability: low operational costs, low maintenance

prices• Manage heterogeneous data/processes access policies• Meet industrial processes requirements

Page 39: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Distributed e-Infrastructures

e-Infrastructures enable researchers at different locations across the worldto collaborate in the context of their home institutions or in national or multinational scientific initiatives.• People can work together having shared access to unique or distributed scientific facilities (including data,

instruments, computing and communications).

Examples:

Belief, http://www.beliefproject.org/OpenAire, http://www.openaire.eu/i-Marine, http://www.i-marine.eu/EU-Brazil OpenBio, http://www.eubrazilopenbio.eu/

Page 40: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

D4Science.org – Hybrid Data Infrastructure

Unified Resource Space

Powered by gCubeEnab

les

Inte

grat

es

D4Science.org Infrastructure

WPS

Variety/Veracity VolumeVelocity/Variability

1. External Systems:• Storage• Computations• Data services

2. Integration services:• Manage external systems• Harmonise data• Host data and processes• Support adaptability

3. Infrastructure resources:• Manage security• Expose Integration services• Support information

exchange between services

Data ComputationalInfrastructures

ComputationalServices

A system of systems

Page 41: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Virtual Research Environments

Inte

grat

es

D4Science.org Infrastructure

Unified Resource Space

Powered by gCubeEnab

les

VRE VRE VRE

WPS

• Define sub-communities

• Allow temporary dedicated assignment of computational, storage, and data resources

• Manage policies

• Support data and information sharing

Page 42: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Virtual Research EnvironmentsInnovative, web-based, community-oriented, comprehensive, flexible, and

secure working environments.

• Communities are provided with applications to interact with the VRE services• Client services are provided both with APIs (Java, R) and simple HTTP-REST interfaces

Page 43: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

D4Science.org Services

Mediators / Adapters

Data Analytics Services Data Space Services

Infrastructures and Service Providers

Collaborative Services Core Services

Resources Mgr

Catalogue

HN

AAA

VRE Mgr

Social Networking Workspace Users Mgmt

Standard based (e.g. CWS)Ad-hoc mediators

Search

Access

Storage

Dashboard

Algorithms

Workflows

Browse

Publish

Curation

Page 44: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

ResearchersD4Science supports scientists in several domains

1. More than 25 000 taxonomicstudies per monthwww.i-marine.eu

2. More than 60 000 species distribution maps produced and hostedwww.d4science.eu

3. Used to build a pan- European geothermal energy mapwww.egip.d4science.org

4. Processing and management of heterogeneous environmental and Earth system data

www.envriplus.eu

5. Enhances communication and exchange in Linguistic Studies, Humanities, Cultural Heritage, History and Archaeologywww.parthenos-project.eu

Page 45: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Society and citizens1. CNR Smart Campus - PISA: a Smart City experiment to optimise the

use of resources and reduce the environmental impact, whilst increasing the quality of life and work. www.smart-applications.area.pi.cnr.it

2. SoBigData EU Prj. : create the Social Mining & Big Data Ecosystem, a research infrastructure for ethic-sensitive scientific discoveries and advanced applications of social data mining. www.sobigdata.eu

data storage and mining of the large data information flow on parking, buildings and mobility

computational platform and cloud storage to integrate data mining processes and host data and results, VA enabler

Page 46: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Policy Makers

1. D4Science hosts and runs the CMSY model to assess the health status of fisheries stocks

http://www.cnr.it/news/index/news/id/5987

CMSY model

2. D4Science supports the identification of Marine Protected Areas to reduce adverse impact of human activities (e.g. fishing, aquaculture, tourism) on ecosystems, and to ensure these activities are properly embedded in policy frameworks.

http://www.bluebridge-vres.eu/services/protected-area-impact-maps

Page 47: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Companies

1. Predict aquaculture revenue and business development

www.bluebridge-vres.eu

2. Host and process satellite data from Copernicus

3. Collect logs from experts and centralize the network of information

4. Self-service integration of algorithms to enable Cloud computation

services.d4science.org

Page 48: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

EducationLecture-style: the course topics stress is different depending on the audience

Interactive: after each explained topic, students do experiments

Experimental: students reproduce the experiment shown by the teacher and possibly repeat it on their own data

Social: students communicate via messaging or VRE discussion panel

• 1 course/yearIn Pisa

• 1 course/yearIn Paris

• 12 coursesIn Copenhagen

www.bluebridge-vres.eu

International Council for the Exploration of the Sea

• 38 coursesAll over the world+1000 attendees

Page 49: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Numbers• +2000 scientists in 44 countries, • integrating +50 heterogeneous

data providers, • executing +25,000

processes/month,• providing access to over a billion

quality records in repositories worldwide,

• 99,7% service availability.• +50 VREs hosted

Page 50: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

StatisticalManager

D4ScienceComputational

FacilitiesSharing

Setup and execution

Computing Platform

Coro, G., Candela, L., Pagano, P., Italiano, A., & Liccardo, L. (2015). Parallelizing the execution of native data mining algorithms for computational biology. Concurrency and Computation: Practice and Experience, 27(17), 4630-4644.

Page 51: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Collaborative experiments

WS

Shared online folders

Inputs

Outputs

Results

Computational system

In the e-Infrastructure

Through third party software

Page 52: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Process description:http://dataminer-d-d4s.d4science.org/wps/WebProcessingService?Request=DescribeProcess&Service=WPS&Version=1.0.0&gcube-token=d7a4076c-e8c1-42fe-81e0-bdecb1e8074a&Identifier=org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.generators.CMSY

Process execution:http://dataminer-d-d4s.d4science.org/wps/WebProcessingService?request=Execute&service=WPS&Version=1.0.0&gcube-token=d7a4076c-e8c1-42fe-81e0-bdecb1e8074a&lang=en-US&Identifier=org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.generators.CMSY&DataInputs=IDsFile=http://goo.gl/9rg3qK;StocksFile=http://goo.gl/Mp2ZLY;SelectedStock=HLH_M07

R/JAVA ClientGuide:https://wiki.gcube-system.org/gcube/How_to_Interact_with_the_Statistical_Manager_by_client#WPS_Client

InterfacesWeb Processing ServiceWeb Interfaces

QGIS

Page 53: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

WPS

REST

I.S.

Infrastructure

Infrastructure resources

Geospatial data

External infra.

WPS

Page 54: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Advantages of integrations The process is available as-a-Service Invoked via communication standards Higher computational capabilities Automatic creation of a Web interface Provenance management Storage of results on a high-availability system Collaboration and sharing Re-usability, e.g. from other software (e.g. QGIS)

Page 55: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Innovation through integrationVision: integration, sharing, and remote hosting help informing people and taking decisions

Page 56: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Using CMSY

https://i-marine.d4science.org/group/drumfish/drumfish

Page 57: CMSY workshop - Gianpaolo Coro (ISTI-CNR)

Thank you!