Download - Machine Reasoning about Anomalous Sensor Data

Machine Reasoning about Anomalous Sensor Data

Matt Calder, Francesco Peri, Bob Morris

Center for Coastal Environmental Sensoring Networks CESNUniversity of Massachusetts Boston

Goal

Provide scientists with software to explore domain hypotheses about their data

Outline1. Outline2. Motivation3. Knowledge Representation4. Our Knowledge System5. Software Architecture6. What’s missing (future work)

UMB CESN

• Interdisciplinary Research effort• Oceanography

• Biology

• Computer Science

• Policy / Law

• Cyber-infrastructure – Smart Sensor Networks

Algal Bloom ?

Benthic Resuspension ?

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

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

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.

Knowledge System

PhysicalPropertyPhysicalProperty

Measurement

Sensor

hasTakencanMeasure

real number dateTime

value timestamp

CESN Sensor Ontology: Core Components

Domain Knowledge Ontology: Ocean Events

OceanEvent

AlgalBloom BenthicResuspension

subClass subClass

dateTime

occurredAtTime

occurredAtLocationInfluencedBy

cesn:Locationcesn:PhysicalProperty

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

Outline

1. Outline2. Motivation3. Knowledge Representation4. Our Knowledge System5. Software Architecture6. What’s missing (future work)

Sensor Data Reasoning System

Outline

1. Outline2. Motivation3. Knowledge Representation4. Our Knowledge System5. Software Architecture6. What’s missing (future work)

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

Summary

• Software System to test domain knowledge hypothesis about Sensor Data•

Thanks. Any Questions?

Key Components

Ontology

Rules

Software – Jena framework