Machine Reasoning about Anomalous Sensor Data Matt Calder, Francesco Peri, Bob Morris Center for...
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Transcript of Machine Reasoning about Anomalous Sensor Data Matt Calder, Francesco Peri, Bob Morris Center for...
![Page 1: Machine Reasoning about Anomalous Sensor Data Matt Calder, Francesco Peri, Bob Morris Center for Coastal Environmental Sensoring Networks CESN University.](https://reader035.fdocuments.us/reader035/viewer/2022062714/56649d625503460f94a4485f/html5/thumbnails/1.jpg)
Machine Reasoning about Anomalous Sensor Data
Matt Calder, Francesco Peri, Bob Morris
Center for Coastal Environmental Sensoring Networks CESNUniversity of Massachusetts Boston
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Goal
Provide scientists with software to explore domain hypotheses about their data
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Outline1. Outline2. Motivation3. Knowledge Representation4. Our Knowledge System5. Software Architecture6. What’s missing (future work)
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UMB CESN
• Interdisciplinary Research effort• Oceanography
• Biology
• Computer Science
• Policy / Law
• Cyber-infrastructure – Smart Sensor Networks
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Outline1. Outline2. Motivation3. Knowledge Representation4. Our Knowledge System5. Software Architecture6. What’s missing (future work)
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Algal Bloom ?
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Benthic Resuspension ?
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Aha!
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Outline1. Outline2. Motivation3. Knowledge Representation4. Our Knowledge System5. Software Architecture6. What’s missing (future work)
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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
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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
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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.
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Outline1. Outline2. Motivation3. Knowledge Representation4. Our Knowledge System5. Software Architecture6. What’s missing (future work)
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Knowledge System
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PhysicalPropertyPhysicalProperty
Measurement
Sensor
hasTakencanMeasure
real number dateTime
value timestamp
CESN Sensor Ontology: Core Components
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Domain Knowledge Ontology: Ocean Events
OceanEvent
AlgalBloom BenthicResuspension
subClass subClass
dateTime
occurredAtTime
occurredAtLocationInfluencedBy
cesn:Locationcesn:PhysicalProperty
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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
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Outline
1. Outline2. Motivation3. Knowledge Representation4. Our Knowledge System5. Software Architecture6. What’s missing (future work)
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Sensor Data Reasoning System
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Outline
1. Outline2. Motivation3. Knowledge Representation4. Our Knowledge System5. Software Architecture6. What’s missing (future work)
![Page 21: Machine Reasoning about Anomalous Sensor Data Matt Calder, Francesco Peri, Bob Morris Center for Coastal Environmental Sensoring Networks CESN University.](https://reader035.fdocuments.us/reader035/viewer/2022062714/56649d625503460f94a4485f/html5/thumbnails/21.jpg)
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
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Summary
• Software System to test domain knowledge hypothesis about Sensor Data•
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Thanks. Any Questions?
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Key Components
Ontology
Rules
Software – Jena framework