1. Semantic sewer pipe failure detection: Linked data approaches for discovering events
Jonathan Yu | Research software engineer Environmental Information Systems, CLW Highett
21 October 2013
CSIRO LAND AND WATER

2. Undetected sewer rising mains pipe failures...
Direct costs: water service providers ($ mil. per event) Indirect costs: social, environmental ($10k - $1 mil. per event)
We can apply event detection over sensor networks for addressing issues in urban contexts such as detecting pipe failures
There is an extensive network of pipes each with varied material compositions, age, and surrounding soil properties,
which makes prediction of pipe failure a little unpredictable.
Performing event detection over real-time sensor data using ontology-driven approaches | Jonathan Yu

3. Sewer rising mains case study
We got hold of data from a recent pipe burst in Victoria, where a pipe failure occurred close to a creek which fed the port phillip bay.
It was undetected for more than a week.
- only detected when a passer-by walking his dog noticed a significant change in the colour of the creek.
The ramification for the water service provider was for them to send trucks in to pump out sewerage until the pipe was fixed, which wasn’t cheap.
- the EPA threatened with hefty fines.
The graph in this slide show the pipe failure event documented with the red line.
The blue line shows the flow rate in litres per second.
- notice an upward trend as it built up to the failure event and then sustained a higher than normal flow rate... for a long time.
Example event: Flow rate > 100 l/s
Performing event detection over real-time sensor data using ontology-driven approaches | Jonathan Yu

4. RDF Triple Store contains ontologies
Semantic Sensor Network
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
Event-detection
SSN Extensions - quantity values
Water domain – flow, pressure, units of measure
Pipe features, observations, sensors defs
We use a set of ontology modules shown here in this slide.
Ontologies offer the ability to import one another, so we can modularise our definitions
– separating sensor definitions from the domain and the application definitions.
The beauty of this is that we can potentially leverage existing ontologies published through the web, if they exist and reuse them and keep our application definitions fairly separate.
We can also separate our user definitions from domain definitions, which enhances the ability to reuse both application and domain semantics.
Pipe domain rules – MCA, risk levels, PVC pipes, asbestos concrete pipes

5. Event Notification Interaction
Internal network
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
Web server
Sensor Middleware (GSN)
Observation and Notification
REST Web service
Event Rules
Virtual Sensors
Real-time sensor data
Sensor Network
SPARQL
Event Dashboard – maps sensors to ontologies
RDF
Triple Store

6. Event Detection Linked Data API
Public accessible network
Internal network
Web Server
Web Server
Event Detection Linked Data
SPARQL
RDF
Triple Store
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
Event Dashboard – maps sensors to ontologies

7. Event Detection Linked Data
Visualization client
Public accessible network
Internal network
Web Server
Web Server
Viz
SPARQL
RDF
Triple Store
Event Detection Linked Data
Event Dashboard – maps sensors to ontologies
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

8. Overall architecture schematic
Public accessible network
Internal network
Web Server
Reverse
Proxy
/ auth
Web Server
Sensor Middleware (GSN)
Observation and Notification
REST Web service
Viz
Event Rules
Virtual Sensors
Real-time sensor data
Event Detection Linked Data
Sensor Network
SPARQL
Event Dashboard – maps sensors to ontologies
RDF
Triple Store
Event
Dashboard

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

10. 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
Availability of real-time sensor data presents many potential applications
Barriers for allowing user-access to event definition over observations
barrier to precise definition of event semantics
Ontologies offer a means to capture semantics of a domain of discourse
- semantics are treated as first class citizens - not implicit in the syntax or code of the user interface, middleware services, databases, documents, text files - Resolving issue of data heterogeneity
- facilitates interoperability between systems - semantics captured in ontologies are independent of software components - separation of concerns and greater flexibility and reuse of software components -- e.g interchange domains
Ontology-driven approaches can assist user-definition of events over a given sensor network
- using familiar user interfaces - browser based
Demonstrated a prototype browser-based user interface
Lowers the barrie for users like domain scientists and managers
- general purpose tool for exploring sensor data in consistent fashion
- Ontology tools allow consistency checking, and inferrencing capability
- Allows Capture semantics of user defined events of interest precisely and consistently
Combining real-time events (dynamic) with domain knowledge (static) using ontologies
Performing event detection over real-time sensor data using ontology-driven approaches | Jonathan Yu

11. Event Detection Ontology def’s: Event Rule, Value Constraints, Units
Slide shows some application ontology definitions.
Value Constraint event rules – which is a simple construct to specify that an event rule may have
-- a feature of interest,
-- a sensor of interest,
-- on an observed property
-- with a value constraint on the observed value.
E.g. Flow rate observations on a pipe flow sensor, on a pipe at Clunies Ross st, greater than 100 l/s.
Performing event detection over real-time sensor data using ontology-driven approaches | Jonathan Yu

12. Domain ontologies (uwda:) - Sensors
In our system, we have defined some sensors specific to the domain.
In the urban water domain, we define Pipe flow sensors, and pump pressure sensors.
Performing event detection over real-time sensor data using ontology-driven approaches | Jonathan Yu

13. Sewer rising mains case study
We got hold of data from a recent pipe burst in Victoria, where a pipe failure occurred close to a creek which fed the port phillip bay.
It was undetected for more than a week.
- only detected when a passer-by walking his dog noticed a significant change in the colour of the creek.
The ramification for the water service provider was for them to send trucks in to pump out sewerage until the pipe was fixed, which wasn’t cheap.
- the EPA threatened with hefty fines.
The graph in this slide show the pipe failure event documented with the red line.
The blue line shows the flow rate in litres per second.
- notice an upward trend as it built up to the failure event and then sustained a higher than normal flow rate... for a long time.
Example event: Flow rate > 100 l/s
Performing event detection over real-time sensor data using ontology-driven approaches | Jonathan Yu

14. Event rule definition instances
Rule ID
Observed property
Value constraint
Feature of interest
Observed By (Sensor) 1
Flow 2
> 100 l/s 3
Pipe A 4
Pipe Sensor A-1 5
Using the definition of a ValueConstraint Event rule class, we can start to describe event rule instances with varying levels of detail.
This allows us to describe event rule instances with varying levels of detail. We can describe an event of interest as all observations observing flow.
Flow greater than 100 l/s
Flow greater than 100 l/s on Feature of interest
Flow greater than 100 l/s on a sensor
Flow greater than 100 l/s on both sensor and feature.
Performing event detection over real-time sensor data using ontology-driven approaches | Jonathan Yu

15. Fusing real-time events with domain knowledge
Real-time data
Sensor Network
Notifications
Event of Interest
Query knowledge base (domain knowledge)
e.g. Populate knowledge base with parameterised historical pipe failure data. Infer likelihood of pipe failure based on physical attributes and known operating environment
Knowledge Base
In many domains, there exists a large amount of expert knowledge, which is quite static compared to the dynamic real-time observation data.
So in our work, we considered incorporating such expert knowledge as an extension to the event detection based on real-time sensor data.
Fusing real-time event notifications with domain knowledge.
- Allows us to perform context-sensitive event detection.
Performing event detection over real-time sensor data using ontology-driven approaches | Jonathan Yu

16. Modelling the feature of interest – pipe materials
Performing event detection over real-time sensor data using ontology-driven approaches | Jonathan Yu

17. Modelling the burst events
Performing event detection over real-time sensor data using ontology-driven approaches | Jonathan Yu

18. Event detections using dynamic and static info
> 200 PSI
Notification:
Location: Pipe X
Risk of burst: LOW +
(Static)
Pipe material is PVC
and
Risk level of pipe is A (good)
Start to create event rules that leverage static domain knowledge captured in the domain rules
For example, fusing real-time pump pressure sensor observations, with knowledge about the state of the pipe that it is pumping into.
Event here : pressure exceeds 200 p.s.i. && pipe is relatively ok
Performing event detection over real-time sensor data using ontology-driven approaches | Jonathan Yu

19. Event detections using dynamic and static info
> 200 PSI
Notification:
Location: Pipe X
Risk of burst: HIGH +
(Static)
Pipe material is PVC
and
Risk level of pipe is E (bad)
Event here : pressure exceeds 200 p.s.i. && pipe is in bad shape
Start to create event rules that leverage static domain knowledge captured in the domain rules
Context-sensitive event detection
Performing event detection over real-time sensor data using ontology-driven approaches | Jonathan Yu

20. Sensor Infrastructure Event Dashboard Notification clients
Initialise sensor network and sensor middleware
Map sensors
Query Notifications
Define event constraint
Visualise Notifications
Deploy event constraint
Integrate with Notification systems
Performing event detection over real-time sensor data using ontology-driven approaches | Jonathan Yu

21. Ontology-enabled User Interface
Future work
/ SMS
Database
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
Existing alert systems
Execute workflow A
Smoothing function
Event B
Ontology-enabled User Interface
Sensor Network
Performing event detection over real-time sensor data using ontology-driven approaches | Jonathan Yu

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

23. Thank you Land and Water Jonathan Yu Paul Davis Brad Sherman
Research Software Engineer
Research Scientist t t e e e w
Land and Water
ICT Centre
Scott Gould
Kerry Taylor
Donavan Marney
Research Projects Officer
Principal Research Scientist
Research team leader t
t +61   t e e e w w
Land and Water