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 1. Outline 2. Motivation 3. Knowledge Representation 4. Our Knowledge System 5. Software Architecture 6. What’s missing (future work)

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

Outline 1. Outline 2. Motivation 3. Knowledge Representation 4. Our Knowledge System 5. Software Architecture 6. What’s missing (future work)

Algal Bloom ?

Benthic Resuspension ?

Aha!

Outline 1. Outline 2. Motivation 3. Knowledge Representation 4. Our Knowledge System 5. Software Architecture 6. What’s missing (future work)

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.

Outline 1. Outline 2. Motivation 3. Knowledge Representation 4. Our Knowledge System 5. Software Architecture 6. What’s missing (future work)

Knowledge System

CESN Sensor Ontology: Core Components

Domain Knowledge Ontology: Ocean Events

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. Outline 2. Motivation 3. Knowledge Representation 4. Our Knowledge System 5. Software Architecture 6. What’s missing (future work)

Sensor Data Reasoning System

Outline 1. Outline 2. Motivation 3. Knowledge Representation 4. Our Knowledge System 5. Software Architecture 6. 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