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Session 5.6 towards a semantic outlier detection framework in wireless sensor networks

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Talk at SEMANTiCS 2017
www.semantics.cc

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Session 5.6 towards a semantic outlier detection framework in wireless sensor networks

  1. 1. SEMANTiCS 2017, Amsterdam, The Netherlands, September 11-14, 2017 Towards a Semantic Outlier Detection Framework in Wireless Sensor Networks Iker Esnaola-Gonzalez, Jesús Bermúdez, Izaskun Fernandez, Santiago Fernandez, Aitor Arnaiz
  2. 2. © IK4-TEKNIKER 2017 Agenda • Introduction • Role of Semantics in Outlier Detection • The SemOD Framework • Temperature Sensor use case • Results • Conclusions
  3. 3. © IK4-TEKNIKER 2017 Introduction • Current datasets suffer from: • Noisy data • Missing data • Outliers • … • Consequences: • Complicate knowledge extraction • Low quality mining results • Inaccurate conclusions • Solution: Preprocessing techniques
  4. 4. © IK4-TEKNIKER 2017 Introduction • Outlier Detection: • Spotting data that stand out among other and do not have the expected behaviour. • What to do with them? • Isolate and act on them (Fraud Detection) • Filter them out (Data Analytics) • …
  5. 5. © IK4-TEKNIKER 2017 Role of Semantics in Outlier Detection • Underlying semantics of data can be exploited to detect outliers • Is a 44ºC measurement an outlier? It depends on the context: • Location • Time • Season • …
  6. 6. © IK4-TEKNIKER 2017 The SemOD Framework • The Semantic Outlier Detection (SemOD) Framework for WSNs • Assists the data scientist in: • Outlier Detection • Outlier Classification
  7. 7. © IK4-TEKNIKER 2017 The SemOD Framework • 3 main components • The EEPSA Ontology • The SemOD Method • The SemOD Query
  8. 8. © IK4-TEKNIKER 2017 The SemOD Framework • Use case: 3 Temperature sensors located in IK4-Tekniker building (Eibar, Spain) • EEPSA Ontology for Semantic Annotation
  9. 9. © IK4-TEKNIKER 2017 The SemOD Framework • Infers sensors vulnerabilities • For each vulnerability, a SemOD Method is proposed • SemOD Method: guide to identify outliers caused by that vulnerability
  10. 10. © IK4-TEKNIKER 2017 • 1st step: Sensor’s sun exposure • Determines periods when sensor may be exposed to sun • The EEPSA Ontology infers them • Depends on sensor location and orientation Temperature Sensor use case
  11. 11. © IK4-TEKNIKER 2017 Temperature Sensor use case • 2nd step: Sunshine constraint • Determines if sensor receives sunshine when enough sun • Derived from nearby sensor’s solar irradiance and illuminance
  12. 12. © IK4-TEKNIKER 2017 Temperature Sensor use case • 3rd step: SemOD Query generation • Fills SemOD Query pattern with information of previous steps • Classifies measurements as outliers caused due to sensor’s sun exposure CONSTRUCT {?obs1 rdf:type eepsa:OutlierCausedBySolarRadiation } FROM <myGRAPH > WHERE { ?sensor1 sosa:observedProperty m3-lite:Temperature . ?sensor2 sosa:observedProperty m3-lite:Illuminance ; eepsa:hasUnitOfMeasure m3-lite:Lux . ?obs1 sosa:isObservedBy ?sensor1 ; eepsa:obsTime ?time ; eepsa:obsDate ?date . ?obs2 sosa:isObservedBy ?sensor2 ; eepsa:obsTime ?time ; eepsa:obsDate ?date ; sosa:hasSimpleResult ?illu …
  13. 13. © IK4-TEKNIKER 2017 Results SENSOR T17 *Classic method: Rapidminer’s Detect Outlier (Densities) operator
  14. 14. © IK4-TEKNIKER 2017 Conclusions • SemOD Framework: Assistance in Outlier Detection and Classification • Exploit underlying semantics of data, not just values. • Not exclusive and complementary to other outlier detection methods • Applicable to multiple domains
  15. 15. © IK4-TEKNIKER 2017 Thank you for your attention Iker Esnaola-Gonzalez iker.esnaola@tekniker.es

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