Semantic sewer pipe failure detection:Linked data approaches for discovering events

CSIRO LAND AND WATER

Jonathan Yu | Research software engineerEnvironmental Information Systems, CLW Highett21 October 2013

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

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

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

Semantic Sensor Network

SSN Extensions - quantity values

Pipe features, observations, sensors defs

Water domain – flow, pressure, units of measure

RDF Triple Store contains ontologies

Event-detection

Pipe domain rules – MCA, risk levels, PVC pipes, asbestos concrete pipes

• These ontologies provide semantics and constructs to describe sensors and observations generally

• General model extended with domain semantics and knowledge

• Allows definitions to be captured explicitly and used consistently.

• Extensible – able to capture more domain knowledge/rules

• E.g. PVC pipe feature definitions and domain rules

SensorMiddleware

(GSN)

Web server

RDFTriple Store

Sensor Network

Real-time sensordata

SPARQL

Observation and

NotificationREST Web

service

Internal network

Event Notification Interaction

• Event rules deployed in GSN send notifications to web service

• Web service adds metadata to notification and sends to RDF Triple Store

• RDF Triple Store persists the sensor observations and event notifications like a semantic knowledge base

Event Rules

VirtualSensors

Web Server Web Server

Event Detection

Linked Data

RDFTriple Store

SPARQL

Internal networkPublic accessible network

Event Detection Linked Data API

• List notifications, list sensor observations, view semantic descriptions of pipes, pumps, observed properties

• allows users to browse contents of a RDF triple store via standard web browser• configured to view sensor observations, event notifications, semantic definitions,

domain knowledge base • Also enables software clients to retrieve JSON/XML/RDF/TXT formats of the same

information for mashups and data fusion activities

Web Server Web Server

Event Detection

Linked Data

Viz

RDFTriple Store

SPARQL

Internal networkPublic accessible network

Visualization client

• Example of a visualization client querying the RDF triple store for sensor observations and event notifications

• Identifiers from the RDF triple store resolve to metadata and semantic definitions delivered via the Event Detection Linked Data API

SensorMiddleware

(GSN)

Web Server Web Server

Event Detection

Linked Data

Viz

RDFTriple Store

Sensor Network

Real-time sensordata

SPARQL

Observation and NotificationREST Web

service

Internal networkPublic accessible network

Reverse Proxy/ auth

EventDashboard

Overall architecture schematic

Event Rules

VirtualSensors

Performing event detection over real-time sensor data using ontology-driven approaches | Jonathan Yu9 |

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

Summary

• Domain & event ontologies: • Defining and capturing sewer pipe event descriptions extending SSN ontology

and

• Linked Data APIs and RESTful services• Publish and discovery of sewer pipe event notifications and observations

• Demo visualization client

• Preliminary work to demo real-time events can be combined with domain knowledge for context sensitive event detection using ontologies

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

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

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

Domain ontologies (uwda:) - Sensors

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

Sewer rising mains case study

13 |

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

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

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

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

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

Modelling the feature of interest – pipe materials

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

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

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

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

Performing event detection over real-time sensor data using ontology-driven approaches | Jonathan Yu20 |

Define event constraint

Map sensorsInitialise sensor

network and sensor middleware

Deploy event constraint

Query Notifications

Visualise Notifications

Integrate with Notification

systems

Event Dashboard Notification clientsSensor Infrastructure

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

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

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A

BEventSmoothing

function

Email / SMS

Database

Execute workflow

Existing alert systems

Ontology-enabledUser Interface

Sensor Network

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

Questions?

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Land and WaterScott GouldResearch Projects Officert +61 3 9252 6103e scott.gould@csiro.auw www.csiro.au/clw

ICT CentreKerry TaylorPrincipal Research Scientistt +61 2 6216 7038e kerry.taylor@csiro.auw www.csiro.au/ict

Land and WaterDonavan MarneyResearch team leadert +61 3 9252 6585e donavan.marney@csiro.auw www.csiro.au/clw

LAND AND WATER

Thank youLand and WaterJonathan YuResearch Software Engineert +61 3 9252 6440e jonathan.yu@csiro.auw www.csiro.au/clw

Land and WaterPaul DavisResearch Scientistt +61 3 9252 6310e paul.davis@csiro.auw www.csiro.au/clw

Land and WaterBrad ShermanResearch Scientist

e Brad.Sherman@csiro.auw www.csiro.au/clw