This document discusses Irstea's publication of weather station data as linked open data using semantic web standards. It provides an overview of open data and linked open data principles. It then describes Irstea's weather station in Montoldre, France, the sensors that collect data, and the observations made. It details how the data was modeled using the Semantic Sensor Network (SSN) ontology and other related ontologies. Finally, it discusses converting the data from CSV files to RDF and making it available via a SPARQL endpoint.
Semantically-Enabling the Web of Things: The W3C Semantic Sensor Network Onto...Laurent Lefort
Presentation of the SSN XG results at eResearch Australia 2011 https://eresearchau.files.wordpress.com/2012/06/74-semantically-enabling-the-web-of-things-the-w3c-semantic-sensor-network-ontology.pdf
Overview of the W3C Semantic Sensor Network (SSN) ontologyRaúl García Castro
The slides include an overview of the W3C Semantic Sensor Network (SSN) ontology along with an example of its use in a coastal flood emergency planning use case in the FP7 SSG4Env project.
A Recommender Story: Improving Backend Data Quality While Reducing CostsDatabricks
A recommender story: improving backend data quality while reducing costsnInformation overload is one of the biggest challenges academics face on a daily basis while finding the right knowledge to advance science. With around 7k research articles being published every day, how do you find the right ones?
Elsevier is a global information analytics business that helps institutions and professionals advance healthcare, open science and improve performance. With many data sources and signals being available, data science and big data engineering provide the perfect opportunity to deliver more value to researchers.
Here we will focus on Mendeley, an open (free of charge) academic content platform to help researchers discover new information via functionalities such as a crowd sourced collection of academic related documents (Catalogue) and various personalized recommender systems. MendeleySuggest, the recommender system, helps millions of researchers worldwide to find documents and people relevant to their research field, they did not yet know exist. The personalised recommenders are powered by Mendeley Catalogue, clustering 2 billion records correctly into canonical records, state of the art algorithms and big data solutions (e.g. Spark).
In the past few years, we noticed that with our content growth, quality of the canonical records started drifting due to scalability issues. As a result, we faced clustering accuracy problems and, in turn, impacting also the recommenders. In this talk we will highlight how we rearchitected the fabrication of Mendeley Catalogue to improve its scalability and accuracy. In addition, we will show how the migration from Hadoop Map Reduce to Spark has helped us reduce costs as well as improving maintainability.
Semantically-Enabling the Web of Things: The W3C Semantic Sensor Network Onto...Laurent Lefort
Presentation of the SSN XG results at eResearch Australia 2011 https://eresearchau.files.wordpress.com/2012/06/74-semantically-enabling-the-web-of-things-the-w3c-semantic-sensor-network-ontology.pdf
Overview of the W3C Semantic Sensor Network (SSN) ontologyRaúl García Castro
The slides include an overview of the W3C Semantic Sensor Network (SSN) ontology along with an example of its use in a coastal flood emergency planning use case in the FP7 SSG4Env project.
A Recommender Story: Improving Backend Data Quality While Reducing CostsDatabricks
A recommender story: improving backend data quality while reducing costsnInformation overload is one of the biggest challenges academics face on a daily basis while finding the right knowledge to advance science. With around 7k research articles being published every day, how do you find the right ones?
Elsevier is a global information analytics business that helps institutions and professionals advance healthcare, open science and improve performance. With many data sources and signals being available, data science and big data engineering provide the perfect opportunity to deliver more value to researchers.
Here we will focus on Mendeley, an open (free of charge) academic content platform to help researchers discover new information via functionalities such as a crowd sourced collection of academic related documents (Catalogue) and various personalized recommender systems. MendeleySuggest, the recommender system, helps millions of researchers worldwide to find documents and people relevant to their research field, they did not yet know exist. The personalised recommenders are powered by Mendeley Catalogue, clustering 2 billion records correctly into canonical records, state of the art algorithms and big data solutions (e.g. Spark).
In the past few years, we noticed that with our content growth, quality of the canonical records started drifting due to scalability issues. As a result, we faced clustering accuracy problems and, in turn, impacting also the recommenders. In this talk we will highlight how we rearchitected the fabrication of Mendeley Catalogue to improve its scalability and accuracy. In addition, we will show how the migration from Hadoop Map Reduce to Spark has helped us reduce costs as well as improving maintainability.
Finnish Meteorological Institute is opening its weather data. Slides kept in Aaltoes Insights event describes first insights about open data portal and what is going to be opened.
Large Scale On-Demand Image Processing For Disaster ReliefRobert Grossman
This is a status update (as of Feb 22, 2010) of a new Open Cloud Consortium project that will provide on-demand, large scale image processing to assist with disaster relief efforts.
Solar System Processing with LSST: A Status UpdateMario Juric
An update for the LSST Solar System Science Collaboration on the work in progress on data products and software needed to support the Solar System science. Delivered at DPS 2017 meeting.
The PRP is a partnership of more than 50 institutions, led by researchers at UC San Diego and UC Berkeley and includes the National Science Foundation, Department of Energy, and multiple research universities in the US and around the world. The PRP builds on the optical backbone of Pacific Wave, a joint project of CENIC and the Pacific Northwest GigaPOP (PNWGP) to create a seamless research platform that encourages collaboration on a broad range of data-intensive fields and projects.
How HPC and large-scale data analytics are transforming experimental scienceinside-BigData.com
In this deck from DataTech19, Debbie Bard from NERSC presents: Supercomputing and the scientist: How HPC and large-scale data analytics are transforming experimental science.
"Debbie Bard leads the Data Science Engagement Group NERSC. NERSC is the mission supercomputing center for the USA Department of Energy, and supports over 7000 scientists and 700 projects with supercomputing needs. A native of the UK, her career spans research in particle physics, cosmology and computing on both sides of the Atlantic. She obtained her PhD at Edinburgh University, and has worked at Imperial College London as well as the Stanford Linear Accelerator Center (SLAC) in the USA, before joining the Data Department at NERSC, where she focuses on data-intensive computing and research, including supercomputing for experimental science and machine learning at scale."
Watch the video: https://wp.me/p3RLHQ-kLV
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Dr. Frank Wuerthwein from the University of California at San Diego presentation at International Super Computing Conference on Big Data, 2013, US Until recently, the large CERN experiments, ATLAS and CMS, owned and controlled the computing infrastructure they operated on in the US, and accessed data only when it was locally available on the hardware they operated. However, Würthwein explains, with data-taking rates set to increase dramatically by the end of LS1 in 2015, the current operational model is no longer viable to satisfy peak processing needs. Instead, he argues, large-scale processing centers need to be created dynamically to cope with spikes in demand. To this end, Würthwein and colleagues carried out a successful proof-of-concept study, in which the Gordon Supercomputer at the San Diego Supercomputer Center was dynamically and seamlessly integrated into the CMS production system to process a 125-terabyte data set.
ArrayUDF: User-Defined Scientific Data Analysis on ArraysGoon83
User-Defined Functions (UDF) allow application programmers to specify analysis operations on data, while leaving the data management and other non-trivial tasks to the system. This general approach is at the heart of the modern Big Data systems, such MapReduce/Spark and SciDB. However, a wide variety of common scientific data operations -- such as computing the moving average of a time series, the vorticity of a fluid flow, etc., -- are hard to express and slow to execute with these Big Data systems. In this talk, we will introduce a brand new Big Data system namely ArrayUDF (https://bitbucket.org/arrayudf/arrayudf) for scientific data sets, especially for multi-dimensional arrays. The ArrayUDF allows flexible expressiveness of UDF for scientific data analysis on the strength of their common character--structural locality. ArrayUDF executes the UDF directly on arrays stored in files, such as HDF5, without any data load overload. ArrayUDF's desi
gn and implementation considerations for parallel data processing on large-scale HPC will also be introduced. The performance tests on Edison at NERSC show that ArrayUDF is around 2000X faster than Spark on processing large scientific datasets.
Présentation faite lors d'une réunion du projet animitex à montpellier en aôut 2014. Cette présentation introduit certains formats du web sémantique en particulier ceux accessible sur le web de données . Ensuite les travaux de Fabien Amarger sur la transformation de SKOS en ontologies OWL sont survollés.
Finnish Meteorological Institute is opening its weather data. Slides kept in Aaltoes Insights event describes first insights about open data portal and what is going to be opened.
Large Scale On-Demand Image Processing For Disaster ReliefRobert Grossman
This is a status update (as of Feb 22, 2010) of a new Open Cloud Consortium project that will provide on-demand, large scale image processing to assist with disaster relief efforts.
Solar System Processing with LSST: A Status UpdateMario Juric
An update for the LSST Solar System Science Collaboration on the work in progress on data products and software needed to support the Solar System science. Delivered at DPS 2017 meeting.
The PRP is a partnership of more than 50 institutions, led by researchers at UC San Diego and UC Berkeley and includes the National Science Foundation, Department of Energy, and multiple research universities in the US and around the world. The PRP builds on the optical backbone of Pacific Wave, a joint project of CENIC and the Pacific Northwest GigaPOP (PNWGP) to create a seamless research platform that encourages collaboration on a broad range of data-intensive fields and projects.
How HPC and large-scale data analytics are transforming experimental scienceinside-BigData.com
In this deck from DataTech19, Debbie Bard from NERSC presents: Supercomputing and the scientist: How HPC and large-scale data analytics are transforming experimental science.
"Debbie Bard leads the Data Science Engagement Group NERSC. NERSC is the mission supercomputing center for the USA Department of Energy, and supports over 7000 scientists and 700 projects with supercomputing needs. A native of the UK, her career spans research in particle physics, cosmology and computing on both sides of the Atlantic. She obtained her PhD at Edinburgh University, and has worked at Imperial College London as well as the Stanford Linear Accelerator Center (SLAC) in the USA, before joining the Data Department at NERSC, where she focuses on data-intensive computing and research, including supercomputing for experimental science and machine learning at scale."
Watch the video: https://wp.me/p3RLHQ-kLV
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Dr. Frank Wuerthwein from the University of California at San Diego presentation at International Super Computing Conference on Big Data, 2013, US Until recently, the large CERN experiments, ATLAS and CMS, owned and controlled the computing infrastructure they operated on in the US, and accessed data only when it was locally available on the hardware they operated. However, Würthwein explains, with data-taking rates set to increase dramatically by the end of LS1 in 2015, the current operational model is no longer viable to satisfy peak processing needs. Instead, he argues, large-scale processing centers need to be created dynamically to cope with spikes in demand. To this end, Würthwein and colleagues carried out a successful proof-of-concept study, in which the Gordon Supercomputer at the San Diego Supercomputer Center was dynamically and seamlessly integrated into the CMS production system to process a 125-terabyte data set.
ArrayUDF: User-Defined Scientific Data Analysis on ArraysGoon83
User-Defined Functions (UDF) allow application programmers to specify analysis operations on data, while leaving the data management and other non-trivial tasks to the system. This general approach is at the heart of the modern Big Data systems, such MapReduce/Spark and SciDB. However, a wide variety of common scientific data operations -- such as computing the moving average of a time series, the vorticity of a fluid flow, etc., -- are hard to express and slow to execute with these Big Data systems. In this talk, we will introduce a brand new Big Data system namely ArrayUDF (https://bitbucket.org/arrayudf/arrayudf) for scientific data sets, especially for multi-dimensional arrays. The ArrayUDF allows flexible expressiveness of UDF for scientific data analysis on the strength of their common character--structural locality. ArrayUDF executes the UDF directly on arrays stored in files, such as HDF5, without any data load overload. ArrayUDF's desi
gn and implementation considerations for parallel data processing on large-scale HPC will also be introduced. The performance tests on Edison at NERSC show that ArrayUDF is around 2000X faster than Spark on processing large scientific datasets.
Présentation faite lors d'une réunion du projet animitex à montpellier en aôut 2014. Cette présentation introduit certains formats du web sémantique en particulier ceux accessible sur le web de données . Ensuite les travaux de Fabien Amarger sur la transformation de SKOS en ontologies OWL sont survollés.
Irstea Use Case: Integration of Crop Observations using Semantic Web Technolo...catherine roussey
Présentation of AgroTechnopole where Irstea develops a use case of data integration of Crop observation. Participation Panel Session on "Semantics to enable sharing and interoperability of data in agriculture.
What do we need?" 10th International Conference on Metadata and Semantics Research 22-25 November 2016, Göttingen, Germany MTSR 2016
Les nouveaux métiers de l'information-documentation : quelques repères.../ADB...Sylvie LAFON
Présentation lors de la soirée ADBS Midi-Pyrénées du 12/12/2013 :
- de 4 nouveaux métiers en information-documentation en émergence (document controller, knowledge manager, records manager, e-reputation)
- les tendances de l'emploi en information-documentation pour la région Midi-Pyrénées
Presentation faite pour la formation enitab a partir d'un chapitre d'ouvrage ROUSSEY, C., FRANÇOIS PINET, KANG, M.A., CORCHO, O. - 2009. How ontologies are used for software interoperability. Chapter to appear in: Use of Ontologies to Support Information Interoperability, Springer, 52 pages disponible ici http://www.towntology.net/towntologyreferences.php
Semantic Sensor Network Ontology: Description et usagecatherine roussey
cours à l'école d'Été Web Intelligence 2013 « Le Web des objets » 3 septembre 2013, Saint-Germain-Au-Mont-d'Or, Franc. 67 slides.
ce cours en plus de décrire l'ontology ssn présente certains usages.
Semantic Web technologies, both those envisaged and those already realised, have the potential to benefit domains where issues such as volume, complexity and heterogeneity can overcome traditional techniques. Sensor networks are one such area where the application of semantics is indicated by scale, complexity, and the need to integrate over heterogeneous standards, sensors and systems for multiple purposes and multiple disciplines.
The Semantic Sensor Networks W3C Incubator is an international initiative to develop standards for sharing information collected by sensors and sensor networks over the Web, including an ontology for different types of sensing devices and their observations, and new approaches for the semantic markup of sensor descriptions and services that support sensor data exchange and sensor network management.
Kerry will describe the ongoing effort to increase the quality and reduce the cost of capturing environmental data, to address the growing demand for information about the environmental systems that support Australia’s agricultural, resource and process-based industries.
Using the Data Cube vocabulary for Publishing Environmental Linked Data on la...Laurent Lefort
Canberra Semantic Web Meetup.
Initiatives have been launched to develop semantic vocabularies representing statistical classifications and discovery metadata. Tools are also being created by statistical organizations to support the publication of dimensional data conforming to the Data Cube specification, now in Last Call at W3C.
The meeting will be an opportunity to hear about two semantic Web and Linked Data initiatives for statistical data that are driven by the Australian Government. The Bureau of Meteorlogy and CSIRO have recently released a Linked Data version of the ACORN-SAT historical climate data at http://lab.environment.data.gov.au and the ABS has released the Census data modelled in the Data Cube vocabulary which is part of a challenge the ABS is organising in context of the SemStats Workshop (http://www.datalift.org/en/event/semstats2013/challenge) at the International Semantic Web Conference (ISWC) in Sydney (http://iswc2013.semanticweb.org).
Come along to hear about these two projects, the challenges encountered and the solutions developed.
As the volume and complexity of data from myriad Earth Observing platforms, both remote sensing and in-situ increases so does the demand for access to both data and information products from these data. The audience no longer is restricted to an investigator team with specialist science credentials. Non-specialist users from scientists from other disciplines, science-literate public, to teachers, to the general public and decision makers want access. What prevents them from this access to resources? It is the very complexity and specialist developed data formats, data set organizations and specialist terminology. What can be done in response? We must shift the burden from the user to the data provider. To achieve this our developed data infrastructures are likely to need greater degrees of internal code and data structure complexity to achieve (relatively) simpler end-user complexity. Evidence from numerous technical and consumer markets supports this scenario. We will cover the elements of modern data environments, what the new use cases are and how we can respond to them.
Fi cloudpresentationgyrardaugust2015 v2Amélie Gyrard
Cross-Domain Internet of Things Application Development: M3 Framework and Evaluation
FiCloud 24-26 August 2015, Rome, Italy
Semantic Web technologies, Semantic Interoperability,
Semantic Web Of Things (SWoT), Internet of Things (IoT), Web of Things (WoT), Machine to Machine (M2M), Ubiquitous Computing, Pervasive Computing, Context Awareness
Linked Open Vocabularies for Internet of Things (LOV4IoT),
Sensor-based Linked Open Rules (S-LOR),
Machine-to-Machine Measurement (M3) framework,
sharing and reusing domain knowledge
Arkady Zaslavsky, Charith Perera, Dimitrios Georgakopoulos, Sensing as a Service and Big Data, Proceedings of the International Conference on Advances in Cloud Computing (ACC), Bangalore, India, July, 2012, Pages 21-29 (8)
Using the Open Science Data Cloud for Data Science ResearchRobert Grossman
The Open Science Data Cloud is a petabyte scale science cloud for managing, analyzing, and sharing large datasets. We give an overview of the Open Science Data Cloud and how it can be used for data science research.
An Autonomic Approach to Real-Time Predictive Analytics using Open Data and ...Wassim Derguech
Public datasets are becoming more and more available for organizations. Both public and private data can be used to drive innovations and new solutions to various problems. The Internet of Things (IoT) and Open Data are particularly promising in real time predictive data analytics for effective decision support. The main challenge in this context is the dynamic selection of open data and IoT sources to support predictive analytics. This issue is widely discussed in various domains including economics, market analysis, energy usage, etc. Our case study is the prediction of energy usage of a building using open data and IoT. We propose a two-step solution: (1) data management: collection, filtering and warehousing and (2) data analytics: source selection and prediction. This work has been evaluated in real settings using IoT sensors and open weather data.
Charith Perera, Arkady Zaslavsky, Peter Christen, Michael Compton, and Dimitrios Georgakopoulos, Context-aware Sensor Search, Selection and Ranking Model for Internet of Things Middleware, Proceedings of the IEEE 14th International Conference on Mobile Data Management (MDM), Milan, Italy, June, 2013
AusCover Earth Observation Services and Data CubesTERN Australia
The presentation provides an overview of earth observation services offered by AusCover Facility of TERN. The presentation was part of the Workshop on Approaches to Terrestrial Ecosystem Data Management : from collection to synthesis and beyond which was held on 9th of March 2016 in University of Queensland.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Weather Station Data Publication at Irstea: an implementation Report.
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
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
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
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
What is 5 star Open Data?
source: Tim Berners-Lee, http://5stardata.info/
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
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
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
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
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
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"?
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
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
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?
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
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
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
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. www.irstea.fr
Pour mieux
affirmer
ses missions,
le Cemagref
devient Irstea
Thanks for your attention!
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
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
Ontology Design Pattern: ODP SSO
STIMULUS SENSOR OBSERVATION
Sensor is anything that observes
How it senses ?
What is sensed?
What senses ?
30. 30
Ontology Design Pattern: SSO in SSN
STIMULUS SENSOR OBSERVATION
Sensor is anything that observes
How it senses ?
What is sensed?
What senses ?