performing event detection over real-time sensor data using ontology-driven approaches csiro land...

Performing event detection over real-time sensor data using ontology-driven approaches

CSIRO LAND AND WATER

Jonathan Yu | Research software engineerEnvironmental Information Systems, CLW Highett

14 May 2013

Performing event detection over real-time sensor data using ontology-driven approaches | Jonathan Yu

Outline

Background: Event detection over real-time sensor data

Capturing machine readable semantics – ontologies

Enabling user-based events detection using ontology-driven approaches

2 |


Performing event detection over real-time sensor data

Real-time observations are increasingly becoming available through sensor networks• Reporting, monitoring, analysis

Examine events that happen in a sensor network and get notifications

• Mitigate risk in the environment

• Improve management response times

3 |

WQ Weather Flow

Sensor Network


Water quality issues...

Example events in this domain:• Total nitrogen conc. in a river > X mg/L• Dissolved oxygen conc. at sensor < Y mg/L

4 |


Undetected sewer rising mains pipe failures...

Direct costs: water service providers ($ mil. per event)

Indirect costs: social, environmental ($10k - $1 mil. per event)

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Sewer rising mains case study

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0 2000 4000 6000 8000 10000 12000 14000 160000

20

40

60

80

100

120

140

160

Time (mins)

Flow

rate

(l/s

)

Pipe failure event = flow > 100 l/s

Example event: Flow rate > 100 l/s


Sensor middleware - Global Sensor Network (GSN)

WQ WeatherFlow

Sensor Network

GSN

Virtual Sensor (WQ)

Virtual Sensor(Flow)

Virtual Sensor (Aggr.)

End users

User Interface

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Stepped notifications: < 5 consecutive = low, 5 - 20 consecutive = moderate, > 20 = high risk

Using GSN for event detection on sewer rising mains

(Low risk)

(High risk)

Stepped Notifications

Flow observations

Simple Moving Average

Looking for when flow exceeds a preset threshold over the Simple Moving average

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Existing workflow: real-time event detection

High level entry for an end user e.g. Scientists and managers• Inefficient

Knowledge hidden behind code or in people’s heads - implicit semantics• Barrier for reusability• Possible inconsistencies

9 |

Curation CodingAnalysis,

Monitoring, Management

SensorMiddleware

(GSN)

Sensor Network

End users

Programmers


Problem of data heterogeneity, integration

Multiple datasets• Often multiple data schemas, formats, field names,

conventions

The use of the observation property “Flow rate”• Flow• FLOW_RATE• RATE_OF_FLOW_L_per_s

Need a mechanism for consistent use of semantics• Map to shared and commonly understood definitions

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Capturing semantics, not just syntax

Syntax of a language refers to the structure or grammarSemantics of a language refers to the meaning

E.g. “Colorless green ideas sleep furiously.”

• Syntactically correct• Semantically meaningless/inconsistent

11 |

Zzzzzz...


Enable capture and consistent use of semantics

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Observation event at Flow Sensor X

Flow

PVC Pipe at Clunies Ross Street

100 litres per second

Flow sensor X

Flow rate sensing

Has an observation result

Some result

Has value

Produced by

implementsobserves

Has observation property

Has feature of interest

Observation event at WQ sensor

Lake Burley Griffin

Another result 10mg/L

WQ meter

Dissolved oxygen sensingDissolved

oxygen conc.

Observation

Feature of interest

Sensor Output Observation Value

Sensor

SensingProperty


OntologiesEnable specification of semanticse.g. Concepts, relationships, logic

assertions

Provides ability to refer to ‘Flow rate’ concept (semantics), rather than FLOW_RATE (syntax)

Machine readable/processable• Using Web Ontology Language (OWL)• W3C standard – “semantic web”

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implementsobserves

Sensor

SensingProperty

Observation

Sensor Output

Has an observation

result

Produced by


Leverage ontology tools

• Query languages like “SPARQL” – SPARQL Protocol and RDF Query

Language– For querying RDF statements

which OWL builds upon

– E.g. Find all sensors that observe flow rate

• Triple stores for storing RDF and ontology statements

• Ontology reasoners– Automated consistency checking– Inference engines

All men are mortal,Socrates is a man=> Socrates is mortal

All sewer pipe bursts near rivers cause some environmental damage,

Pipe X is near River Y,Pipe X has a pipe burst event=> River X has caused some

environmental damage at River Y

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Enabling “user-based” real-time event detection

Enable capture of user-defined semantics of events & reference sensors consistently

1) Sensor network system semantics described (e.g. Flow rate sensor is located at X)

2) Domain of interest semantics described (e.g. Flow is an observable property)

3) Event semantics described (e.g. Flow rate at sensor#1 > 100 l/s)

4) Machine-readability: for rendering in user interfaces & code generation

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SensorMiddleware

(GSN)

Sensor Network

End users

?


Return notifications from triggered events with metadata based on ontology semantics

e.g. The sewer rising main has a problem due to <...>

Ontology-driven event detection system

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SensorMiddleware

(GSN)

Sensor Network

End users

Ontology-enabledUser Interface

OntologiesSemantic

Sensor Net. Ontology

DomainOntology

Annotates available sensors and their capabilities

e.g. Flow rate sensor data at Location X

Generate code for event detection using event constraint semanticse.g. FLOW_RATE > 100 l/s

Populate user interface elements based on domain semantics and sensor network annotations.

Allow users to define event constraints


Semantic Sensor

Network

Event-detection

WQdomain

Urban water domain

WQ user Urban water user

Mid-Upper ontologies

Application ontologies Domain ontologies

User ontologies

Representing domains and applications

???

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Event Detection Ontology def’s:Event Rule, Value Constraints, Units

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Domain ontologies (uwda:) - Sensors

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Event rule definition instances

Rule ID Observed property

Value constraint

Feature of interest

Observed By (Sensor)

1 Flow

2 Flow > 100 l/s

3 Flow > 100 l/s Pipe A

4 Flow > 100 l/s Pipe Sensor A-1

5 Flow > 100 l/s Pipe A Pipe Sensor A-1

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Event dashboard - User interface demo for urban water domain

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Performing event detection over real-time sensor data using ontology-driven approaches | Jonathan Yu22 |


Ontology APIs

Ontology-driven user interface

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SensorMiddleware

(GSN)


Triple store(User ontologies)

Ontology Reasoner

Ontology definitions

End usersQuery/Rule engines

Presentation Widgets(Standard web forms)


Advantages of ontology-driven approach• End users can focus more on exploring real-time datasets

• Semantics are explicitly specified and transferrable

• User interface allows domain ontologies to be interchangeable

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Curation Coding Analysis, Monitoring, Management

Curation CodingAnalysis,

Monitoring, Management


Fusing real-time events with domain knowledge

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KnowledgeBase

Sensor NetworkReal-time data

Event of Interest

Query knowledge base(domain knowledge)

Notifications

e.g. Populate knowledge base with parameterised historical pipe failure data.

Infer likelihood of pipe failure based on physical attributes and known operating environment


Modelling the feature of interest – pipe materials

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Event detections using dynamic and static info

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> 200 PSI

+

Pipe material is PVCand

Risk level of pipe is A (good)

(Dynamic)

(Static)

Notification:

Location: Pipe XRisk of burst: LOW


Event detections using dynamic and static info

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> 200 PSI

+

Pipe material is PVCand

Risk level of pipe is E (bad)

(Dynamic)

(Static)

Notification:

Location: Pipe XRisk of burst: HIGH


Summary

• Availability of real-time sensor data presents many potential applications

• Ontologies offer a means to capture semantics of a domain of discourse

• Ontology-driven approaches can assist user-definition of events over a given sensor network and consistent use of domain, application, sensor network semantics

• Shown how real-time events can be combined with domain knowledge for context sensitive event detection using ontologies

30 |


Future work

Notification handling• Messaging queue systems• Attaching metadata based on

event rule semantics

More complex events• Event semantics • Incorporate processing-filters

User studies to evaluate the user interface

Deployments on actual sensor networks

31 |

A

BEvent

Smoothing function

Email / SMS

Database

Execute workflow

Existing alert systems


Sensor Network


Questions?

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Land and WaterScott GouldResearch Projects Officert +61 3 9252 6103e [email protected] www.csiro.au/clw

ICT CentreKerry TaylorPrincipal Research Scientistt +61 2 6216 7038e [email protected] www.csiro.au/ict

Land and WaterDonavan MarneyResearch team leadert +61 3 9252 6585e [email protected] www.csiro.au/clw

LAND AND WATER

Thank youLand and WaterJonathan YuResearch Software Engineert +61 3 9252 6440e [email protected] www.csiro.au/clw

Land and WaterPaul DavisResearch Scientistt +61 3 9252 6310e [email protected] www.csiro.au/clw

Land and WaterBrad ShermanResearch Scientist

e [email protected] www.csiro.au/clw

performing event detection over real-time sensor data using ontology-driven approaches csiro land...

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