1 / 24

Machine Reasoning about Anomalous Sensor Data

Machine Reasoning about Anomalous Sensor Data. Matt Calder, Francesco Peri, Bob Morris Center for Coastal Environmental Sensoring Networks CESN University of Massachusetts Boston. Goal. Provide scientists with software to explore domain hypotheses about their data. Outline. Outline

garth
Télécharger la présentation

Machine Reasoning about Anomalous Sensor Data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Machine Reasoning about Anomalous Sensor Data Matt Calder, Francesco Peri, Bob Morris Center for Coastal Environmental Sensoring Networks CESN University of Massachusetts Boston

  2. Goal Provide scientists with software to explore domain hypotheses about their data

  3. Outline • Outline • Motivation • Knowledge Representation • Our Knowledge System • Software Architecture • What’s missing (future work)

  4. UMB CESN • Interdisciplinary Research effort • Oceanography • Biology • Computer Science • Policy / Law • Cyber-infrastructure – Smart Sensor Networks

  5. Outline • Outline • Motivation • Knowledge Representation • Our Knowledge System • Software Architecture • What’s missing (future work)

  6. Algal Bloom ?

  7. Benthic Resuspension ?

  8. Aha!

  9. Outline • Outline • Motivation • Knowledge Representation • Our Knowledge System • Software Architecture • What’s missing (future work)

  10. Knowledge Representation • An ontology is a model of the relationships between concepts (ideas) of a particular domain. • OWL Web Ontology Language from the W3C • Classes, Properties, Instances

  11. Semantic Reasoners • Validation • Checks that the constraints made in the ontology are not violated • For example, a temperature sensor should not have taken any measurements other than temperature measurements. • Inference and Rules • An inference is a conclusion drawn from the the truth value of previously known facts • antecedent -> consequence • A ∧ B ∧ C -> D

  12. Rule Example in Jena RL • [winter rule: • (?x measurementOf Temperature) • (?x type Average), • (?x value ?v), • lessThan(?v, 0) → • (Season isWinter true) ] In English: If x is a temperature and is an average and has value v and v is less than 0 then it is winter.

  13. Outline • Outline • Motivation • Knowledge Representation • Our Knowledge System • Software Architecture • What’s missing (future work)

  14. Knowledge System

  15. CESN Sensor Ontology: Core Components

  16. Domain Knowledge Ontology: Ocean Events

  17. By the way… Was it an Algal Bloom? ….No. It was winter! Was it bethic diatom resuspension? Maybe – That is consistent with data and knowledge

  18. Outline Outline Motivation Knowledge Representation Our Knowledge System Software Architecture What’s missing (future work)

  19. Sensor Data Reasoning System

  20. Outline Outline Motivation Knowledge Representation Our Knowledge System Software Architecture What’s missing (future work)

  21. To Be Done • Distributed Sensor Reasoning Systems • Integrate with a stronger observations ontology such as OBOE Ontology from SEEK • User Interfaces for Rules • Investigate scalability and performance of large sensor data sets. • Integrate with our existing SOS server • Collaborate with others

  22. Summary • Software System to test domain knowledge hypothesis about Sensor Data

  23. Thanks. Any Questions?

  24. Key Components • Ontology • Rules • Software – Jena framework

More Related