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www.irstea.fr 
Pour mieux 
affirmer 
ses missions, 
le Cemagref 
devient Irstea 
Catherine ROUSSEY, Stephan BERNARD, Géral...
2 
Outline 
• Irstea needs 
• a data provider 
• From open data to linked open data 
• State of the art about meteorologic...
3 
Irstea: an environmental data provider 
Irstea uses and provides several datasets. 
Teams belongs to several environmen...
4 
What is Open Data? 
Open data is data that can be freely used, reused and redistributed by 
anyone - subject only, at m...
5 
What is 5 star Open Data? 
source: Tim Berners-Lee, http://5stardata.info/
6 
How to build 5 star Open Data 
1. Prepare Stakeholders 
2. Select a dataset 
3. Model the data. 
4. Specify an appropri...
7 
Linked Open Data cloud 
An extension of the 
current Web… 
… where data are given 
well-defined and 
explicitly represe...
8 
State of the Art SSN 
SSN FOR PUBLISHING METEOROLOGICAL DATA 
Feature of interest, spatial, time 
• AEMET (Agencia Esta...
9 
Irstea Weather Station 
MONTOLDRE 
Montoldre center of France 
Vantage Pro 2 of Davis Instruments 
Sensors: 
• temperat...
10 
Irstea Weather Station
11 
Semantic Sensor Network Ontology
12 
Network of Ontologies 
Semantic Sensor Network : the backbone 
Sensing Device 
ontology for meteorological sensor (aws...
13 
Description of Weather Station 
SSN + LOCATION + GEOMETRY 
What is a weather station? 
It is a ssn:Platform, ssn:Syste...
14 
Description of Weather Station 
SSN + LOCATION + GEOMETRY
15 
Description of sensors 
SSN + AWS + CF-PROPERTY 
Which type of sensor ? 
• It is hard to find the specific type of sen...
16 
Description of Sensors
17 
Description of Observation 
SSN (DUL) + CF-FEATURE +CF-PROPERTY+ QU 
Observation describes the context of measurement....
18 
Description of Observation
19 
Description of Observation 
SSN + TIME 
Observation describes the context of measurement. 
When the measure was done? ...
20 
Time Instant Observation
21 
Time Interval Observation
22 
Convert data to linked data representation 
TRANSFORMATION FROM CSV TO RDF 
• Timestamps and duration 
creation 
• Win...
23 
Provide Machine Access to Data 
DEMO 
http://ontology.irstea.fr 
select weather data 
SPARQL endpoint 
http://ontology...
24 
Recommendations 
• Find a set of ontologies that are build to be connected together 
• Never create a new class, just ...
25 
Conclusion & Perspectives 
Not so easy to do it well ! 
Promote our dataset 
• find a correct licence 
• Publish it in...
www.irstea.fr 
Pour mieux 
affirmer 
ses missions, 
le Cemagref 
devient Irstea 
Thanks for your attention!
27 
W3C Semantic Sensor Incubator Group 
: SSN XG 
SSN – XG : mars 2009 
41 Participants de 16 organisations : Des grands ...
28 
Semantic Sensor Network Ontology 
Format OWL 2, disponible sur le web et documentée 
(!!) Orientée capteur uniquement,...
29 
Ontology Design Pattern: ODP SSO 
STIMULUS SENSOR OBSERVATION 
Sensor is anything that observes 
How it senses ? 
What...
30 
Ontology Design Pattern: SSO in SSN 
STIMULUS SENSOR OBSERVATION 
Sensor is anything that observes 
How it senses ? 
W...
31 
DUL et SSN
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Weather Station Data Publication at Irstea: an implementation Report.

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Publié le

Réunion du réseau MIA, 14 octobre 2014, Montpellier.

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Weather Station Data Publication at Irstea: an implementation Report.

  1. 1. www.irstea.fr Pour mieux affirmer ses missions, le Cemagref devient Irstea Catherine ROUSSEY, Stephan BERNARD, Géraldine ANDRE, Oscar CORCHO, Gil DE SOUSA, Daniel BOFFETY , Jean-Pierre CHANET October 13th 2014 Weather Station Data Publication at Irstea: an implementation Report Thanks to Jean Paul CALBIMONT, W3C SSN Working Group and SSN rewievers
  2. 2. 2 Outline • Irstea needs • a data provider • From open data to linked open data • State of the art about meteorological dataset publication • Dataset • Weather dataset from montoldre weather station • Csv files • Model the data, use standard vocabularies • Semantic Sensor Network (SSN) ontology • Networks of ontologies around SSN: SSN+GeoSPARQL+locn, SSN+ AWS+ Climate and Forecast, SSN+ QU+ Time • Convert data to linked data representation • Conclusion and Perspectives
  3. 3. 3 Irstea: an environmental data provider Irstea uses and provides several datasets. Teams belongs to several environmental observatories. • Data Base about avalanche • BDOH Data Base about hydrology https://bdoh.irstea.fr/ • Data about soil pollution Scientific data may be used by other public and research institutes Scientific data  open data (non proprietary format)  linked open data (linked RDF)
  4. 4. 4 What is Open Data? Open data is data that can be freely used, reused and redistributed by anyone - subject only, at most, to the requirement to attribute and sharealike. • Availability and Access: the data must be available as a whole and at no more than a reasonable reproduction cost, preferably by downloading over the internet. The data must also be available in a convenient and modifiable form. • Reuse and Redistribution: the data must be provided under terms that permit reuse and redistribution including the intermixing with other datasets. • Universal Participation: everyone must be able to use, reuse and redistribute - there should be no discrimination against fields of endeavour or against persons or groups. source: Open Data Handbook, http://opendatahandbook.org/en/what-is-open-data/
  5. 5. 5 What is 5 star Open Data? source: Tim Berners-Lee, http://5stardata.info/
  6. 6. 6 How to build 5 star Open Data 1. Prepare Stakeholders 2. Select a dataset 3. Model the data. 4. Specify an appropriate open data license 5. Create good URIs for Linked Data 6. Use standard vocabularies 7. Convert data to a Linked Data representation. 8. Provide machine access to data 9. Announce the new data sets on an authoritative domain 10. Recognize the social contract Hyland, B., Atemezing G, & Villazón-Terrazas B (2014) Best Practices for Publishing Linked Data. W3C Working Group Note. http://www.w3.org/TR/ld-bp/
  7. 7. 7 Linked Open Data cloud An extension of the current Web… … where data are given well-defined and explicitly represented meaning, … … so that it can be shared and used by humans and machines, ... ... better enabling them to work in cooperation And clear principles on how to publish data
  8. 8. 8 State of the Art SSN SSN FOR PUBLISHING METEOROLOGICAL DATA Feature of interest, spatial, time • AEMET (Agencia Estatal de Meteorologia) AEMET, WGS84,Geobuddies, W3C Time • Swiss Experiment project SWEET, WGS84, QUDT • ACORN-SAT (Australian Bureau of Meteorology) WGS84, UK Intervals, DUL, Data Cube • SMEAR (Finnish Station for Measuring Ecosystem Atmosphere Relations) SWEET, Geoname, WGS84,DUL, Data Cube, Situation Theory
  9. 9. 9 Irstea Weather Station MONTOLDRE Montoldre center of France Vantage Pro 2 of Davis Instruments Sensors: • temperature  outdoor temperature • atmospheric pressure  external pressure • air humidity outdoor  relative humidity • weathervane  wind direction • anemometer  wind speed • rain gauge  precipitation quantity + precipitation rate • solar radiation  solar radiation Measurement from 2010 to 2013, every 30 minutes convertion of CSV files
  10. 10. 10 Irstea Weather Station
  11. 11. 11 Semantic Sensor Network Ontology
  12. 12. 12 Network of Ontologies Semantic Sensor Network : the backbone Sensing Device ontology for meteorological sensor (aws) Feature of Interest Climate and Forecast (cf-feature + cf-property) Platform location GeoSPARQL and Location Core Vocabulary (geosparql + locn) Observation W3C Time Ontology (time) Observation value Library of Quantity Kind and Units (qu + dim) Dolce Ultra Light (dul)
  13. 13. 13 Description of Weather Station SSN + LOCATION + GEOMETRY What is a weather station? It is a ssn:Platform, ssn:System. • Platform is not the set of software uses to manage the sensor nodes  Platform is an entity to which other entities can be attached Where is the weather station?  The location is always associated to a Platform individual • WGS84 vocabulary usage does not make the difference between the spatial feature and its geometrical representation (a point). Spatial feature may have several geometrical representations depending of the scale (point, polygon etc…)  Spatial queries : Where are the sensors near "Clermont Ferrand"?
  14. 14. 14 Description of Weather Station SSN + LOCATION + GEOMETRY
  15. 15. 15 Description of sensors SSN + AWS + CF-PROPERTY Which type of sensor ? • It is hard to find the specific type of sensor. • Documentation is incomplete and not precise enough. What type of phenomenum observes sensor?  Cf-property individuals are not declared as instances of ssn:Property class No problem the constraint on the property ssn:observes will infers that these individuals are instances of ssn:Property class Which station belongs the sensors?  The property ssn:onPlatform should be used between a sensor and the weather station • Query: How many sensors onPlatform lesPalanquinsVP2_1? no results
  16. 16. 16 Description of Sensors
  17. 17. 17 Description of Observation SSN (DUL) + CF-FEATURE +CF-PROPERTY+ QU Observation describes the context of measurement. Which sensor do the measurement ? What is measured? What is the measured data? What is the unit of the data ? • Dul properties and qu properties are redondants: which one should be used and why? • Lots of (blank) nodes between the observation and the data value • Hard to find an URI pattern for observation : at_Time_of_Plateform_Sensor_on_Property A sensor (rain gauge) can observe several properties
  18. 18. 18 Description of Observation
  19. 19. 19 Description of Observation SSN + TIME Observation describes the context of measurement. When the measure was done? A measurement can be a instant event: temperature, pressure, humidity A measurement may be an interval event: precipitation quantity, precipitation rate, wind direction, wind speed, solar radiation. • Lack of documentation (wind direction) Aggregation queries: Find the strange days? What are the day where the average temperature is above the monthly expected temperature? Find the days where the farmer can not go working (too much precipitation or wind) Give me the date where the daily quantity precipitation is above a threshold?
  20. 20. 20 Time Instant Observation
  21. 21. 21 Time Interval Observation
  22. 22. 22 Convert data to linked data representation TRANSFORMATION FROM CSV TO RDF • Timestamps and duration creation • Wind direction conversion • Split by month
  23. 23. 23 Provide Machine Access to Data DEMO http://ontology.irstea.fr select weather data SPARQL endpoint http://ontology.irstea.fr/weather/snorql/ Rdf server jena fuseki No reasoner Dataset 8 type of measurement * 48 measurements per day * 365 days * 4 years= 560 640 observations 9 300 000 triplets
  24. 24. 24 Recommendations • Find a set of ontologies that are build to be connected together • Never create a new class, just reference existing classes from others ontologies • Good URI are not so easy • Define pattern (see cooluri) • Create URI for individual with / only (#?) • No Blank Nodes in order to browse the dataset • Review your dataset with several reviewers (ssn workshop)
  25. 25. 25 Conclusion & Perspectives Not so easy to do it well ! Promote our dataset • find a correct licence • Publish it in datahub Use it at a benchmark to run aggregation queries New dataset about hydrology Query a dataset in french and in natural language One day to publish a dataset Ok we do it in 6 months
  26. 26. www.irstea.fr Pour mieux affirmer ses missions, le Cemagref devient Irstea Thanks for your attention!
  27. 27. 27 W3C Semantic Sensor Incubator Group : SSN XG SSN – XG : mars 2009 41 Participants de 16 organisations : Des grands noms du domaine des ontologies et des réseaux de capteurs : CSIRO, Wright State University, OGC, DERI, OEG, Knoesis etc… Objectifs: • Proposer un modèle unifié de données de capteurs et de métadonnées • Etat de l’art sur les ontologies de capteurs existantes • Proposer des méthodes de développements applications intelligentes travaillant sur les données de capteurs Résultat : une ontologie qui intègre plusieurs ontologies existantes, validées dans des projets. Final Report 28 June 2011 http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/
  28. 28. 28 Semantic Sensor Network Ontology Format OWL 2, disponible sur le web et documentée (!!) Orientée capteur uniquement, compatible avec les standards de OGC Aligner sur l’ontologie de haut niveau Dolce Ultra Light (DUL)  Faciliter l’intégration avec d’autres ontologies  SSN ne s’utilise jamais seule (!!), chaque application ne réutilise qu’une sous partie de l’ontologie Ontologie modulaire basé sur des patrons de conception (Design Pattern)  Importe que les parties nécessaires  Faciliter l’évolution de l’ontologie  Répond à plusieurs cas d’usage (4)  Permettre d’avoir plusieurs niveaux de description  « Redondance » voulue et nécessaire Semantic Sensor Network Ontology: http://www.w3.org/2005/Incubator/ssn/ssnx/ssn M. Compton et al. The SSN ontology of the W3C semantic sensor network incubator group. Web Semantics: Science, Services and Agents on the World Wide Web Volume 17, December 2012, pp 25–32
  29. 29. 29 Ontology Design Pattern: ODP SSO STIMULUS SENSOR OBSERVATION Sensor is anything that observes How it senses ? What is sensed? What senses ?
  30. 30. 30 Ontology Design Pattern: SSO in SSN STIMULUS SENSOR OBSERVATION Sensor is anything that observes How it senses ? What is sensed? What senses ?
  31. 31. 31 DUL et SSN

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