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BuildingSemantic Sensor Webs and Applications<br />ESWC 2011 Tutorial<br />29 May 2011<br />
Tutorial Objectives<br /><ul><li>Knowledgeof thebasicconcepts and toolstobuildsemantically-enabledapplications and service...
Whom of thisgroup are you in?
Developerswhowishtobuildsuchapplications
Peopleinterested in thebasicconcepts of semantic sensor web applications
Experts in Semantic Sensor Web applications</li></li></ul><li>Whomwe are?<br /><ul><li>Oscar Corcho (UPM)
Alasdair Gray (UNIMAN)
KostisKyzirakos (NKUA)
Jean Paul Calbimonte (UPM)
Kevin Page (SOTON)</li></li></ul><li>Schedule fortoday<br /><ul><li>Introduction (20’)
Semantic Sensor Web components (20’)
DiscoveringSourcesfor a Region: (20 minutes theory + 30 minutes practical)
Coffeebreak (20 minutes)
QueryingStreaming Data throughOntologies: (20 minutes theory + 30 minutes practical)
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Tutorial ESWC2011 Building Semantic Sensor Web - 01 - Introduction

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Transcript of "Tutorial ESWC2011 Building Semantic Sensor Web - 01 - Introduction"

  1. 1. BuildingSemantic Sensor Webs and Applications<br />ESWC 2011 Tutorial<br />29 May 2011<br />
  2. 2. Tutorial Objectives<br /><ul><li>Knowledgeof thebasicconcepts and toolstobuildsemantically-enabledapplications and servicesthatrelypartiallyortotallyondata comingfrom sensor networks
  3. 3. Whom of thisgroup are you in?
  4. 4. Developerswhowishtobuildsuchapplications
  5. 5. Peopleinterested in thebasicconcepts of semantic sensor web applications
  6. 6. Experts in Semantic Sensor Web applications</li></li></ul><li>Whomwe are?<br /><ul><li>Oscar Corcho (UPM)
  7. 7. Alasdair Gray (UNIMAN)
  8. 8. KostisKyzirakos (NKUA)
  9. 9. Jean Paul Calbimonte (UPM)
  10. 10. Kevin Page (SOTON)</li></li></ul><li>Schedule fortoday<br /><ul><li>Introduction (20’)
  11. 11. Semantic Sensor Web components (20’)
  12. 12. DiscoveringSourcesfor a Region: (20 minutes theory + 30 minutes practical)
  13. 13. Coffeebreak (20 minutes)
  14. 14. QueryingStreaming Data throughOntologies: (20 minutes theory + 30 minutes practical)
  15. 15. Sensor Data and SemanticMashups: (20 minutes theory + 30 minutes practical)</li></li></ul><li>IntroductiontotheSemantic Sensor Web<br />ESWC 2011 Tutorial<br />29 May 2011<br />
  16. 16. Sensor Networks<br /><ul><li>Increasingavailability of cheap, robust, deployablesensors as ubiquitousinformationsources
  17. 17. Dynamic and reactive, butnoisy, and unstructured data streams</li></ul>Source: Antonis Deligiannakis<br />
  18. 18. The Sensor Web<br /><ul><li>Sensor networksmaybenetworked, mostlywireless, hence global and integrated
  19. 19. Universal, web-based access to sensor data
  20. 20. Each network with some kind of authority and administration
  21. 21. Sensor networks vs robust networks</li></ul>6<br />Source: Adaptedfrom Alan Smeaton’sinvitedtalk at ESWC2009<br />
  22. 22. Sensor Web: Isthispart of the Web/Internet?<br />7<br />Source: SemsorGrid4Env consortium<br />
  23. 23. Who are theendusers of sensor networks?<br />Theclimatechangeexpert, or a simple citizen<br />Source: Dave de Roure<br />
  24. 24. Most of you are computer scientists. Why is it worth working on this?<br /><ul><li>You may like helping scientists, or…
  25. 25. You want to address any of the following challenges in Computer Science: </li></ul>Scale, scalable<br />Autonomic behaviour versus control <br />Persistent, heterogeneous, evolving<br />Deployment challenge<br />Some mobile devices<br />Source: Dave de Roure<br />
  26. 26. A set of challenges in sensor data management<br /><ul><li>Data Provisioning</li></ul>Complexity of acquisition: distributed sources, data volumes, uncertainty, data quality, incompleteness <br />Pre-processing incoming data: calibration on instruments (specific), lack of re-grid, calibration, gap-filling features<br />Tools for data ingestion needed: generic, customizable, provide estimates, uncertainty degree, etc.<br /><ul><li>Spatial/temporal
  27. 27. Analysis, modeling</li></ul>Discovery: identify sources, metadata<br />Data quality: gaps, faulty data, loss, estimates<br />Analysis models <br />Republish analytic results, computations, <br />Workflows for data stream processing <br />10<br />Source: Data Management in theWorldWide Sensor Web. Balazinska et al. IEEE Pervasive Computing, 2007<br />
  28. 28. A set of challenges in sensor data management<br /><ul><li>Interoperability</li></ul>Data aggregation/integration<br /><ul><li>Uncertainty, data quality</li></ul>Noise, failures, measurement errors, confidence, trust<br /><ul><li> Distributed processing </li></ul>High volume, time critical<br />Fault-tolerance<br />Load management <br />Stream processing features<br />Continuous queries<br />Live & historical data<br />11<br />Source: Data Management in the WorldWide Sensor Web. Balazinska et al. IEEE Pervasive Computing, 2007<br />
  29. 29. A semanticperspectiveonthesechallenges<br /><ul><li>Sensor data querying and (pre-)processing</li></ul>Data heterogeneity Data integration and fusion<br />Data quality<br />New inferencecapabilitiesrequiredtodealwith sensor information<br /><ul><li>Sensor data modelrepresentation and management</li></ul>For data publication, integration and discovery<br />Bridgingbetween sensor data and ontologicalrepresentationsfor data integration Abstractionlevel<br />Eventmodels<br /><ul><li>Rapid development of applications
  30. 30. Userinteractionwith sensor data</li></ul>Source: FiveChallengesfortheSemantic Sensor Web. García-Castro R, Corcho O.Semantic Web Journal, 2010<br />
  31. 31. Challenges. A high-levelapplication<br />Mashupdevelopment<br />Registry<br />InformationIntegration<br />Sensor Network Ontologies<br />
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