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.
Presentation done at the 9th Summer School on Ontological Engineering and the Semantic Web (SSSW2012, http://sssw.org/) in July 2012. Please do treat references to people (e.g., Manfred Hauswirth) and nationalities (e.g., about Swiss) in the context in which they were done.
The Implementing AI: Hardware Challenges, hosted by KTN and eFutures, is the first event of the Implementing AI webinar series to address the challenges and opportunities that realising AI for hardware present.
There will be presentations from hardware organisations and from solution providers in the morning; followed by Q&A. The afternoon session will consist of virtual breakout rooms, where challenges raised in the morning session can be workshopped.
Artificial Intelligence now impacts every aspect of modern life and is key to the generation of valuable business insights.
Implementing AI webinar series is designed for people involved in the management and implementation of AI based solutions – from developers to CTOs.
Find out more: https://ktn-uk.co.uk/news/just-launched-implementing-ai-webinar-series
This session talks about how to define a problem as a machine learning one. What are the steps toward reaching a satisfying solution from data preparation, feature engineering, evaluating suitable algorithms until releasing the model and putting it in practice. It presents a case study and go through some algorithms mostly implemented in Python.
By Hussein Natsheh - Data Mining entrepreneur, scholar, and founder of CiApple
YouTube video: https://youtu.be/NGbyeX4kpU4
These are my slides for the 2012 meeting of all german DFG founded research training groups (Graduiertenkolleg) in computer science. I present the group METRIK.
Presentation done at the 9th Summer School on Ontological Engineering and the Semantic Web (SSSW2012, http://sssw.org/) in July 2012. Please do treat references to people (e.g., Manfred Hauswirth) and nationalities (e.g., about Swiss) in the context in which they were done.
The Implementing AI: Hardware Challenges, hosted by KTN and eFutures, is the first event of the Implementing AI webinar series to address the challenges and opportunities that realising AI for hardware present.
There will be presentations from hardware organisations and from solution providers in the morning; followed by Q&A. The afternoon session will consist of virtual breakout rooms, where challenges raised in the morning session can be workshopped.
Artificial Intelligence now impacts every aspect of modern life and is key to the generation of valuable business insights.
Implementing AI webinar series is designed for people involved in the management and implementation of AI based solutions – from developers to CTOs.
Find out more: https://ktn-uk.co.uk/news/just-launched-implementing-ai-webinar-series
This session talks about how to define a problem as a machine learning one. What are the steps toward reaching a satisfying solution from data preparation, feature engineering, evaluating suitable algorithms until releasing the model and putting it in practice. It presents a case study and go through some algorithms mostly implemented in Python.
By Hussein Natsheh - Data Mining entrepreneur, scholar, and founder of CiApple
YouTube video: https://youtu.be/NGbyeX4kpU4
These are my slides for the 2012 meeting of all german DFG founded research training groups (Graduiertenkolleg) in computer science. I present the group METRIK.
PLM Open Hours - Systemtechnische Integration von Aktor-Sensor-Listen: BMK im 3DIntelliact AG
Die Grundvoraussetzung für eine sinnvolle Verknüpfung eindeutig identifizierbarer Komponenten sind folgende PLM Themenstellungen:
Mechatronische Systeme mit Integration eindeutiger physischer Komponenten
Funktionale Beschreibung von Systemen mit integrierten Aktoren und Sensoren
Know How über die exakte Systemkonfiguration d.h. Version, Teilenummer und Stückliste der interagierenden Komponenten
In diesen PLM Open Hours soll ein Review über die existierenden Technologien und Lösungen vorgenommen werden.
Smart Sensor capteurs électriques pour systèmes d’attelage par autoprestige-u...autoprestige
nouvelle génération de capteurs électriques pour systèmes d’attelage : le SMART-SENSOR. Grâce au SMART-SENSOR, qui a nécessité trois ans de recherche et de développement, BOSAL DISTRIBUTION lance une solution de connectique complétement adaptée aux véhicules actuels.
http://www.autoprestige-utilitaire.fr/categories.php?path=3
Mit dem Raster von IMMO-SENSOR® erfassen wir für unsere Kunden die Problemstellung von Potenzialimmobilien anhand von 100 Merkmalen systematisch und stellen das Ergebnis stark vereinfacht dar. Auf dieser soliden Grundlage erarbeiten wir Potenzialanalysen, liefern Entscheidungsgrundlagen und erarbeiten Konzepte. Jan Baumgartner, Baumgartner Immobilien-Management GmbH, Wydlerweg 17, 8047 Zürich
Story Lab - Sensor Journalism [23-04-2015, Liège]Gregory Berger
Présentation de 3kd à la Master Class sur les nouvelles narrations.
Comment l'utilisation de capteurs électroniques peut influencer le story telling, l'investigation ou le fact checking.
ineltec Forum 2013, Mittwoch, 11. September 2013, 12.30 – 13.30 Uhr
Fokus Gebäudeautomation
LED: Neues Licht, neue Fragen
Veranstalter: ET Licht und KNX Swiss
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.
PLM Open Hours - Systemtechnische Integration von Aktor-Sensor-Listen: BMK im 3DIntelliact AG
Die Grundvoraussetzung für eine sinnvolle Verknüpfung eindeutig identifizierbarer Komponenten sind folgende PLM Themenstellungen:
Mechatronische Systeme mit Integration eindeutiger physischer Komponenten
Funktionale Beschreibung von Systemen mit integrierten Aktoren und Sensoren
Know How über die exakte Systemkonfiguration d.h. Version, Teilenummer und Stückliste der interagierenden Komponenten
In diesen PLM Open Hours soll ein Review über die existierenden Technologien und Lösungen vorgenommen werden.
Smart Sensor capteurs électriques pour systèmes d’attelage par autoprestige-u...autoprestige
nouvelle génération de capteurs électriques pour systèmes d’attelage : le SMART-SENSOR. Grâce au SMART-SENSOR, qui a nécessité trois ans de recherche et de développement, BOSAL DISTRIBUTION lance une solution de connectique complétement adaptée aux véhicules actuels.
http://www.autoprestige-utilitaire.fr/categories.php?path=3
Mit dem Raster von IMMO-SENSOR® erfassen wir für unsere Kunden die Problemstellung von Potenzialimmobilien anhand von 100 Merkmalen systematisch und stellen das Ergebnis stark vereinfacht dar. Auf dieser soliden Grundlage erarbeiten wir Potenzialanalysen, liefern Entscheidungsgrundlagen und erarbeiten Konzepte. Jan Baumgartner, Baumgartner Immobilien-Management GmbH, Wydlerweg 17, 8047 Zürich
Story Lab - Sensor Journalism [23-04-2015, Liège]Gregory Berger
Présentation de 3kd à la Master Class sur les nouvelles narrations.
Comment l'utilisation de capteurs électroniques peut influencer le story telling, l'investigation ou le fact checking.
ineltec Forum 2013, Mittwoch, 11. September 2013, 12.30 – 13.30 Uhr
Fokus Gebäudeautomation
LED: Neues Licht, neue Fragen
Veranstalter: ET Licht und KNX Swiss
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.
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
Amit Sheth with TK Prasad, "Semantic Technologies for Big Science and Astrophysics", Invited Plenary Presentation, at Earthcube Solar-Terrestrial End-User Workshop, NJIT, Newark, NJ, August 13, 2014.
Like many other fields of Big Science, Astrophysics and Solar Physics deal with the challenges of Big Data, including Volume, Variety, Velocity, and Veracity. There is already significant work on handling volume related challenges, including the use of high performance computing. In this talk, we will mainly focus on other challenges from the perspective of collaborative sharing and reuse of broad variety of data created by multiple stakeholders, large and small, along with tools that offer semantic variants of search, browsing, integration and discovery capabilities. We will borrow examples of tools and capabilities from state of the art work in supporting physicists (including astrophysicists) [1], life sciences [2], material sciences [3], and describe the role of semantics and semantic technologies that make these capabilities possible or easier to realize. This applied and practice oriented talk will complement more vision oriented counterparts [4].
[1] Science Web-based Interactive Semantic Environment: http://sciencewise.info/
[2] NCBO Bioportal: http://bioportal.bioontology.org/ , Kno.e.sis’s work on Semantic Web for Healthcare and Life Sciences: http://knoesis.org/amit/hcls
[3] MaterialWays (a Materials Genome Initiative related project): http://wiki.knoesis.org/index.php/MaterialWays
[4] From Big Data to Smart Data: http://wiki.knoesis.org/index.php/Smart_Data
Leveraging Open Source Technologies to Enable Scientific Archiving and Discovery; Steve Hughes, NASA; Data Publication Repositories
The 2nd Research Data Access and Preservation (RDAP) Summit
An ASIS&T Summit
March 31-April 1, 2011 Denver, CO
In cooperation with the Coalition for Networked Information
http://asist.org/Conferences/RDAP11/index.html
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.
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
PhD subject of Jie Sun. Simulation tool based on JADE , jess rule engine and ontology. The goal is to prove that a sensor that can adapt its behaviour based on observed phenomenon state will libve longer
Présentation faite lors d'une réunion du projet animitex à Montpellier en aôut 2014. Cette présentation brosse un apercu des standards du web sémantique disponible sur le web de données. Puis nous introduisons brièvement les travaux de Fabien Amarger sur la transformation de SKOS en ontologie.
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.
Présentation du projet de l'irstea sur l'annotation des bulletins d'alerte ag...catherine roussey
annotation des Bulletins de Santé du Végétal en utilisant les technologies web sémantique. Objectif final développer le web de données agricol en proposant des ontologies dédiées et des méthodes d'enrichissement et de mises à jour propres à ce domaine
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
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
20240609 QFM020 Irresponsible AI Reading List May 2024
Semantic Sensor Network Ontology: Description et usage
1. Semantic Sensor Network
Ontology: description et
usage
Catherine ROUSSEY
4 septembre 2013
Pour mieux
affirmer
ses missions,
le Cemagref
devient Irstea
www.irstea.fr
Merci à
slide share,
Jean Paul CALBIMONT,
Oscar CORCHO,
W3C SSN Working Group
3. 3
Définitions:
DONNÉES, INFORMATIONS, CONNAISSANCES
Donnée: un élément d‟information,
percevable,
manipulable
Information: donnée +
sens + contexte
type
Connaissance: information +
stabilité + croyance
abstraction + traitement
généralisation d‟un ensemble d‟information = modèle
toujours propre à une personne
partagée par d‟autres personnes
4. 4
Schéma général
DONNÉES, INFORMATIONS, CONNAISSANCES
Connaissances
Résultat d‟un processus d‟apprentissage: une
généralisation d‟un ensemble d‟information que
l‟on va mémoriser
Information
Sens dans un contexte
Données
Perception
Connaissances en IA
Classes en POO
BD Relationnelle
Données typées
Données
Des traitement particuliers sur les données
qualitatives
Description sous forme d‟attribut (description
quantitative & qualitative ) + méthodes
(traitements)
Données fortement structurées optimisées pour le
stockage
Différent niveau de granularité : information
structurée non structurées
5. 5
Définition
ONTOLOGIE
Ontologie avec un O majuscule (philosophie):
Une science: une branche de la métaphysique qui a pour objectif
l‟étude de l‟être, c'est-à-dire l'étude des propriétés générales de tout
ce qui est…
Ontologies au pluriel avec un o minuscule (informatique):
Outils informatiques
résultat d‟une modélisation d‟un domaine d‟étude
défini pour un objectif donné
acceptée par une communauté d‟utilisateurs
…
6. 6
Ontologies …
Gruber 1993 : « une ontologie est une spécification explicite d’une
conceptualisation »
•
•
Conceptualisation: modèle abstrait du domaine: quelles entités?
Spécification explicite: les types et leurs contraintes d’usage sont définis
dans un langage…
Exemples:
•
•
•
Un thésaurus : vocabulaire normalisé
Un schéma de BD : un modèle structuré d'un domaine
Un système expert : un modèle du domaine formalisé pour les
inférences, des conditions exprimées à l'aide de formules logiques
Ontologie linguistique, ressource termino-ontologique, ontologie de
domaine, ontologie de haut niveau, un vocabulaire de métadonnées…
Thomas R. Gruber. “A translation approach to portable ontology
specifications”, Knowledge Acquisition, Volume 5, Issue 2, June 1993, Pages 199–
220
7. 7
Motivation: Ontologie
UNE ONTOLOGIE DE CAPTEURS POURQUOI FAIRE ?
Promouvoir un accès universel et uniformisé des données de capteurs
par le web:
• publier les données sur le web
• interroger ces données avec des techno web
• intégrer les données de capteurs avec d'autres données
• traiter ces données (par exemple les nettoyer pour améliorer leur
qualité)
Une ontologie contient un vocabulaire et un schéma de données:
• consensuels,
• publiés sur le web et documentés
• formalisés avec des standards du web (RDF, OWL, SPARQL)
• Avec des contraintes en DL (conditions nécessaires et/ou suffisantes)
= un schéma de données pour le web de données
8. 8
Définition: Le web de données Linked Data
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
9. 9
Publication sur le web de données
4 Principes:
• Use URIs as names for things
• Use HTTP URIs so that people can look up those names.
• When someone looks up a URI, provide useful information, using the
standards (RDF*, SPARQL)
•
Dereferenceable URI
• Include links to other URIs, so that they can discover more things.
10. 10
Motivation: flux et métadonnées
QU'EST CE QUE SONT LES DONNÉES DE CAPTEURS ?
•Flux de données (Data Stream)
•
•
•
•
Données issues de mesure
Données continues, potentiellement infinie
Données avec des estampilles temporelles (time stamped tuple)
Données bruitées (noisy)
(t9, a1, a2, ... , an)
• un réseau produit plusieurs flux hétérogènes
•
Station météo: précipitation, direction du vent
(t8, a1, a2, ... , an)
(t7, a1, a2, ... , an)
...
...
(t1, a1, a2, ... , an)
...
...
•Métadonnées: données sur les données
•
•
Description du réseau de capteurs : localisation, nb de nœuds
Description des nœuds: niveau d'énergie, sondes, paramétrage des sondes
13. 13
Motivation: Interrogation
Flux de données: requête continue
• fenêtre temporelle
•Les dernières données
(t9, a1, a2, ... , an)
(t8, a1, a2, ... , an)
(t7, a1, a2, ... , an)
...
...
(t1, a1, a2, ... , an)
...
...
Réseau de capteurs:
• ressources limitées: énergie, traitement, stockage
• exécution distribuée des requêtes
• routage, optimisation
Query
• Interrogation
•
•
•
native en utilisant API propre
stockage des flux dans une BD
publication sur le web de données
Window
[t7 - t9]
14. 14
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/
15. 15
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
19. 19
Les autres ontologies nécessaires
•
•
•
•
Ontologies d‟unités
Ontologies géographiques de position et de lieux
Classification de tous les types de sondes
Ontologies des phénomènes observés et de leurs propriétés
SSN est une base pour construire une ontologie d‟application
20. 20
Ontology Design Pattern: ODP SSO
STIMULUS SENSOR OBSERVATION
Sensor is anything that observes
What is sensed?
What senses ?
How it senses ?
21. 21
Ontology Design Pattern: SSO in SSN
STIMULUS SENSOR OBSERVATION
Sensor is anything that observes
What is sensed?
What senses ?
How it senses ?
32. 32
Data + Sensor discovery and linking
SWISS EXPERIMENT : ENVIRONMENTAL RESEARCH
Sensor metadata
swissex:Sensor1
rdf:type ssn:Sensor;
ssn:onPlatform swissex:Station1;
ssn:observes [rdf:type sweetSpeed:WindSpeed].
swissex:Sensor2
rdf:type ssn:Sensor;
ssn:onPlatform swissex:Station1;
ssn:observes [rdf:type sweetTemp:Temperature].
swissex:Station1
:hasGeometry [rdf:type wgs84:Point;
wgs84:lat "46.8037166";
wgs84:long "9.7780305"].
station
33. SSN Use Cases:
Data discovery and linking
Sensor Device selection and discovery
Application et projet
Pour mieux
affirmer
ses missions,
le Cemagref
devient Irstea
www.irstea.fr
34. 34
SSN Uses Case: data discovery and linking
FLOOD RISK ALERT: SEMSORGRID4ENV
Emergency
planner
Real-time
data
Wave,
Wind,
Tide
Meteorological
forecasts
Detect conditions likely to cause a flood
Example:
• “provide me with the wind speed observations average over the
last minute, if it is higher than the average of the last 2 to 3 hours”
35. 35
SSN Uses Case: data discovery and linking
SEMSORGRID4ENV PROJECT WWW.SEMSORGRID4ENV.EU
Emergency
planner
Jeung
H., Sarni, S., Paparrizos, I., Sathe, S., Aberer, K., Dawes,
N., Papaioannus, T., Lehning, M.Effective Metadata
Management
in
federated
Sensor
Networks. in SUTC, 2010
36. 36
SSN Use Cases: Sensor Discovery
SWISSEXPERIMENT
Distributed environment: GSN Davos, GSN Zurich, etc.
•
•
In each site, a number of sensors available
Each one with different schema
Metadata stored in wiki
•
Federated metadata management:
Jeung
H., Sarni, S., Paparrizos, I., Sathe, S., Aberer, K., Dawes,
N., Papaioannus, T., Lehning, M.Effective Metadata
Management
in
federated
Sensor
Networks. in SUTC, 2010
38. 38
Data + Sensor discovery and linking
SWISS EXPERIMENT : ENVIRONMENTAL RESEARCH
Geo
Researcher
Real-time
data
Snow,
Wind,
Radiation.
Lots of stuff
Provide data to create models and compare them to real data
Example:
• “I want to calculate how much snow is lost by evaporation
• So provide me with the snow quantity observations and the air
temperature observations in the station near Geneva over the last
year ”
42. Stream and SPARQL:
interrogation sur le sensor
web
J P Calbimonte PhD Thesis
Pour mieux
affirmer
ses missions,
le Cemagref
devient Irstea
www.irstea.fr
Jean-Paul Calbimonte, Hoyoung Jeung, Óscar Corcho, Karl Aberer: Enabling Query
Technologies for the Semantic Sensor Web. Int. J. Semantic Web Inf. Syst. 8(1): 4363 (2012)
43. 43
Management of heterogeneous data
STATE OF THE ART:
DS
MS
DQP
QP
S-RDF
Ontology-based
Data Access
Heterogeneous
data Integration
R2O +
ODEMapster
Streaming Data
Access
Distributed Query
Processing
SNEE/SNEEql
q
Semantic
Integrator
RDF Streams
Querying
C-SPARQL
extensions
44. 44
Extention SPARQL pour les flux
STATE OF THE ART
SNEEql
RSTREAM SELECT id, speed, direction
FROM wind[NOW];
Streaming SPARQL
PREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#>
SELECT ?sensor ?speed ?direction
FROM STREAM <http://…/SensorReadings.rdf> WINDOW RANGE 1 MS SLIDE 1 MS
WHERE {
?sensor a fire:WindSensor;
fire:hasMeasurements ?WindSpeed, ?WindDirection.
?WindSpeed a fire:WindSpeedMeasurement;
fire:hasSpeedValue ?speed;
fire:hasTimestampValue ?wsTime.
?WindDirection a fire:WindDirectionMeasurement;
fire:hasDirectionValue ?direction;
fire:hasTimestampValue ?dirTime.
FILTER (?wsTime == ?dirTime)
}
C-SPARQL
REGISTER QUERY WindSpeedAndDirection AS
PREFIX fire: <http://www.semsorgrid4env.eu/ontologies/fireDetection#>
SELECT ?sensor ?speed ?direction
FROM STREAM <http://…/SensorReadings.rdf> [RANGE 1 MSEC SLIDE 1 MSEC]
WHERE { …
45. 45
How to deal with Linked Stream/Sensor Data
Ingredients
• An ontology model
• Good practices in URI definition
• Supporting semantic technology
•
•
•
SPARQL extensions
To handle time and tuple windows
To handle spatio-temporal constraints
• REST APIs to access it
Another example: semantically enriching GSN
A couple of lessons learned
46. 46
Lessons Learned
• Sensor data is yet another good source of data with some special
properties
• Everything that we do with our relational datasets or other data
sources can be done with sensor data
• Manage separately data and metadata of the sensors
• Data should always be separated between realtime-data and
historical-data
• Use the time format xsd:dateTime and the time zone
• Graphical representation of data for weeks or months is not trivial
anyway
47. 47
Ontology-based Streaming Data Access
SPARQLStream algebra(S1 S2 Sm)
q
Query
translation
Client
SPARQLStream (Og)
Stream-to-Ontology
Mappings
R2RML
[triples]
Data
translation
Target query/ request
SNEEql
Query Evaluator
Sensor
Network (S1)
Relational
DB (S2)
Stream
Engine (S3)
[tuples]
Ontology-based Streaming Data Access Service
RDF Store
(Sm)
48. 48
Enabling Ontology-based Access to Stream
Example: “provide me with the wind speed observations over the last
minute in the Solent Region ”
cd:Observation
cd:observationResult
cd:observedProperty
xsd:double
cd:Property
cd:featureOfInterest
cd:Feature
cd:locatedInRegion
cd:Region
PREFIX cd:
<http://www.semsorgrid4env.eu/ontologies/CoastalDefences.owl#>
PREFIX sb: <http://www.w3.org/2009/SSNXG/Ontologies/SensorBasis.owl#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
SELECT ?windspeed ?windts
FROM STREAM <http://www.semsorgrid4env.eu/ccometeo.srdf>
[ NOW – 1 MINUTE TO NOW – 0 MINUTES ]
WHERE
{
?WindObs a cd:Observation;
cd:observationResult ?windspeed;
cd:observationResultTime ?windts;
cd:observedProperty ?windProperty;
cd:featureOfInterest ?windFeature.
?windFeature a cd:Feature;
cd:locatedInRegion cd:SolentCCO.
?windProperty a cd:WindSpeed.
}
49. 49
Enabling Ontology-based Access to Stream
RDF-Stream
...
...
( <si-1,pi-1, oi-1>, ti-1 ),
( <si, pi, oi>, ti ),
( <si+1,pi+1, oi+1>, ti+1 ),
...
...
Example: “provide me with the wind speed observations over the last minute in
the Solent Region ”
cd:Observation
cd:observationResult
xsd:double
STREAM
<http://www.semsorgrid4env.eu/ccometeo.srdf>
...
...
( <ssg4e:Obs1,rdf:type, cd:Observation>, ti ),
( <ssg4e:Obs1,cd:observationResult,”34.5”>, ti ),
( <ssg4e:Obs2,rdf:type, cd:Observation>, ti+1 ),
( <ssg4e:Obs2,cd:observationResult,”20.3”>, ti+1 ),
...
...
50. 50
Query translation
envdata_westbay
Feature
envdata_chesil
v
envdata_milford
v
envdata_hornsea
v
envdata_rhylflats
v
Timestamp: long
Observation
hasObservation
Result
Mapping
Hs : float
Lon: float
Lat: float
observedProperty
xsd:float
locatedIn
Region
Region
WaveHeightProperty
SPARQL stream
PREFIX cd: <http://www.semsorgrid4env.eu/ontologies/CoastalDefences.owl#>
PREFIX sb: <http://www.w3.org/2009/SSN-XG/Ontologies/SensorBasis.owl#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
SELECT ?waveheight ?wavets ?lat ?lon
FROM STREAM <http://www.semsorgrid4env/ccometeo.srdf>
WHERE
{
?WaveObs a cd:Observation;
cd:observationResult ?waveheight;
cd:observationResultTime ?wavets;
cd:observationResultLatitude ?lat;
cd:observationResultLongitude ?lon;
cd:observedProperty ?waveProperty;
cd:featureOfInterest ?waveFeature.
?waveFeature a cd:Feature;
cd:locatedInRegion cd:SouthEastEnglandCCO.
?waveProperty a cd:WaveHeight.
}
SNEEql
(SELECT Lon,timestamp,Hs,Lat FROM envdata_rhylflats) UNION
(SELECT Lon,timestamp,Hs,Lat FROM envdata_hornsea) UNION
(SELECT Lon,timestamp,Hs,Lat FROM envdata_milford) UNION
(SELECT Lon,timestamp,Hs,Lat FROM envdata_chesil) UNION
(SELECT Lon,timestamp,Hs,Lat FROM envdata_perranporth) UNION
(SELECT Lon,timestamp,Hs,Lat FROM envdata_westbay) UNION
(SELECT Lon,timestamp,Hs,Lat FROM envdata_pevenseybay)
53. Extention de SSN
Wireless Semantic Sensor
Ontology
Rimel BENDADOUCHE PhD Thesis
Pour mieux
affirmer
ses missions,
le Cemagref
devient Irstea
www.irstea.fr
Bendadouche et al; SSN 2012
54. 54
Wireless Sensor Network (WSN)
NEEDS AND OBJECTIVES
Adapt the WSN node behavior to the context:
•
•
Node state
Phenomena state
Context: ”The context is a set of entities states or information
describing an environment where an event occurs”
State: ”The state is a qualitative data, which changes over time
summarizing a set of information”
SSN'12
12/11/2012
Enhance the lifetime and the good functioning of the network
WSN State of the art Extension of the SSN ontology Use of the WSSN ontology
55. 55
What is a context ?
FLOOD PHENOMENA
FLOOD PHENOMENA STATE:
1. “Normal”
2. “Waiting for rise in water levels”
3. “Rise in water levels”
4. “Flood warning”
NODE (ENERGY) STATE:
1. Strong Energy state
2. Average Energy state
3. Low Energy state
56. 56
Wireless Sensor Network (WSN)
Phenomena state Normal
<weather> node
sends its
measures
<weather>
node sends
nothing
WSN State of the art Extension of the SSN ontology Use of the WSSN ontology
57. 57
WSN and its devices
SSN'12
12/11/2012
WSN State of the art Extension of the SSN ontology Use of the WSSN ontology
61. 61
The use of the WSSN ontology
USING TOOLS
• Develop the WSSN ontology
•
Protégé
• JESS rule engine
•
Derive the state from the sensor data
• Simulate the WSN and its nodes behaviour
•
JADE Simulator
WSN State of the art Extension of the SSN ontology Use of the WSSN ontology
63. 63
Project: Sensei
INTEGRATING THE PHYSICAL WITH THE DIGITAL WORLD OF THE
NETWORK OF THE FUTURE
•
•
•
•
Smart Cities: Transport, energy consumption etc…
the EU's 7 Framework Programme
January 2008 December 2010
19 partners from 11 European countries
http://www.sensei-project.eu/
Zhang, Y., Meratnia, N.and Havinga, P.J.M.(2010) „Ensuring high sensor data quality
through use of online outlier detection techniques‟,Int. J. Sensor Networks, Vol. 7, No.
3, pp.141–151
Bahrepour, Majid and Meratnia, Nirvana and Havinga, Paul J.M. (2010) Fast and Accurate
Residential Fire Detection Using Wireless Sensor Networks. Environmental Engineering
and Management Journal, 9 (2). pp. 215-221. ISSN 1582-9596
64. 64
Project: KNOESIS Semantic Sensor Web
http://knoesis.wright.edu/
J. Pschorr, C. Henson, H. Patni and A. Sheth Sensor Discovery on Linked
Data. Kno.e.sis Center, Wright University, Dayton, USA, 2010.
65. 65
Project: SPITFIRE
SEMANTIC WEB INTERACTION WITH REAL OBJECTS
http://spitfire-project.eu/
SmartServiceProxy
aggregate semantic sensor data into representations of real-world things
called Semantic Entities
provide RESTful direct access to them.
Not yet publicly accessible
66. 66
Project: 52 North
SEMANTIC WEB INTERACTION WITH REAL OBJECTS
http://52north.org/
Sensor Observation Service:
publication of sensor data in RDF
SWEET ontology
Janowicz, K. , Bröring, A., Stasch, C., Schade, S ., Everding, T., & A. Llaves
(2011): A RESTful Proxy and Data Model for Linked Sensor Data.
International Journal of Digital Earth, pp. 1 - 22.
Arne Bröring, Patrick Maué, Krzysztof Janowicz, Daniel Nüst, and Christian
Malewski . Semantically-Enabled Sensor Plug & Play for the Sensor Web
Sensor Plug&Play framework
Sensors 2011, 11(8), pp. 7568-7605.
67. 67
Conclusion & Perspectives
SSN Ontology used in several projects for publishing data sensor on the
web of data…
Some works has to be done:
• good practices in URL definition
• Vizualisation of spatio temporal data
• Distributed reasoning
Follows the Semantic Sensor Network Workshop at ISWC
• SSN13 October 2013 Sydney
• SSN12 http://knoesis.org/ssn2012/
• SSN11 http://ceur-ws.org/Vol-839/
• SSN10 http://ceur-ws.org/Vol-668/
• SSN 2009 http://ceur-ws.org/Vol-522/
• SSN 2006 http://www.ict.csiro.au/ssn06/