1. Detecting sewer rising main events using an ontology-driven event processing system CSIRO LAND AND WATER Jonathan Yu | Research software engineer Paul Davis, Irina Emelyanova, Scott Gould, Kerry Taylor, Donavan Marney 22 Feb 2013

2. The problem with sewer rising mains Sewer rising main / pressure sewers pipeline that carries sewerage at pressure from a pumping station transport sewage where gravity flow is not possible or practical Failures can be severe Direct costs: water service providers ($ mil. per event) –Pipeline repair – rising mains can be long... ~10km+ –Sewerage removal by contractor: say 3 weeks = 12 runs/day [24/7] @ 10 kL per run Indirect costs: social, environmental ($10k - $1 mil. per event) –In a recent case, a burst in a relatively new pressure sewer led to undetected sewage discharge to a nearby creek for approximately 3 months Detecting sewer rising main events using an ontology-driven event processing system | Jonathan Yu 2 |

3. Addressing sewer rising main events An option: Retro-fit commercial pressure sewer monitors Costly and time-consuming... Detecting sewer rising main events using an ontology-driven event processing system | Jonathan Yu 3 |

4. Q: Can we extract value from ‘business-as-usual’ data for early detection and pre-empting of these failures? Detecting sewer rising main events using an ontology-driven event processing system | Jonathan Yu 4 | In most cases, data is already collected Wet-well levels Pressure at pump station Flow rate Look for breakpoints in sewer inflow rate timeseries by analysing trend analysis Capture these rules/event conditions

5. Our approach 1)Investigate how to detect of sewer rising main events Trend analysis methods and algorithms Stream processing engines to enable real-time detection 2)System for user-definition and deployment of event constraints Capture semantics of event constraints System for deployment of event constraints on stream processing engine Propose ontology-driven approach 5 | Detecting sewer rising main events using an ontology-driven event processing system | Jonathan Yu

6. Sewer rising mains case study Prototyping event detection algorithms Sewer rising mains pipe burst detection – flow, pump pressure Simple Moving Average, Exceeded thresholds, Breakpoint analysis and Near Real-Time Disturbance detection (Irina) Detecting sewer rising main events using an ontology-driven event processing system | Jonathan Yu 6 |

7. Stepped notifications: 20 = high risk Applying Simple Moving Average & Stepped notifications Detecting sewer rising main events using an ontology-driven event processing system | Jonathan Yu 7 | (Low risk) (High risk) Stepped Notifications Flow observations Simple Moving Average Looking for when flow exceeds a preset threshold over the Simple Moving average

8. Applying “Near Real-Time Disturbance detection” [1] Detecting sewer rising main events using an ontology-driven event processing system | Jonathan Yu 8 | [1]. Verbesselt J, Zeileis A, Herold M (2011). Near Real-Time Disturbance Detection in Terrestrial Ecosystems Using Satellite Image Time Series: Drought Detection in Somalia. Working Paper 2011-18. Working Papers in Economics and Statistics, Research Platform Empirical and Experimental Economics, Universitaet Innsbruck. http://EconPapers.RePEc.org/RePEc:inn:wpaper: 2011-18. Submitted to Remote Sensing and Environment.http://EconPapers.RePEc.org/RePEc:inn:wpaper Start of monitoring periodHistorical data (input)

9. Applying Near Real-Time Disturbance detection pt. 2 Detecting sewer rising main events using an ontology-driven event processing system | Jonathan Yu 9 | Historical data (input) Monitoring period Break-point identified

10. Use of stream processing engine (GSN) Detecting sewer rising main events using an ontology-driven event processing system | Jonathan Yu 10 | Sensor Middleware (GSN) Sensor Network End users Open source sensor middleware. Provides abstraction APIs on raw streaming sensor data (windowing, aggregate sensor sources, low-level processing libraries, flexible output options) Real-time streaming sensor data Implements event detection algorithms (Scripting via R, Groovy) Email, SMS, Text2Speech, Integration with existing monitoring systems

11. Real-time sensor stream data processing High level entry for an end user e.g. Scientists and managers Knowledge hidden behind code or implicit in people’s heads Possible barrier for reusability Detecting sewer rising main events using an ontology-driven event processing system | Jonathan Yu 11 | CurationCoding Analysis, Monitoring, Management Sensor Middleware (GSN) Sensor Network End users Programmers

12. Problem of data heterogeneity, integration Multiple datasets Often multiple data schemas and formats Example: The use of the observation property “Flow rate” Flow FLOW_RATE RATE_OF_FLOW_L_per_s Want to be able to have mechanism of translating and mapping differing fields, labels to something commonly understood Enhance interoperability Semantics-based approach for defining complex event rules for algal bloom detection | Jonathan Yu 12 |

13. “Semantics-based approach” Ontologies Capture semantics Lingua franca Machine readable/processable Vocabulary of things you care about in your data/system E.g. Ability to refer to ‘Flow rate’ concept, rather than FLOW_RATE We use ontologies for: Providing translation between fields within sensors, datasets Defining event rules Generating code for actioning event rules on the sensors Semantics-based approach for defining complex event rules for algal bloom detection | Jonathan Yu 13 | Ontologies Semantic Sensor Net. Ontology Domain Ontology

14. Ontology-driven event detection system Detecting sewer rising main events using an ontology-driven event processing system | Jonathan Yu 14 | Sensor Middleware (GSN) Sensor Network End users Ontology-enabled User Interface Ontologies Semantic Sensor Net. Ontology Domain Ontology Annotates available sensors and their capabilities e.g. Pump pressure sensor data at Location X Generate appropriate code to perform event detection on available sensors using event constraint semantics e.g. Identify break-point in sewer inflow rate according to trend Populates user interface elements based domain semantics and sensor network annotations. Allow users to define event constraints using ontology semantics Return notifications from triggered events with metadata based on ontology semantics e.g. The sewer rising main has a problem due to

15. Detecting sewer rising main events using an ontology-driven event processing system | Jonathan Yu 15 |

16. Future work Implementation of breakpoint analysis and “near real-time disturbance” algorithms in our system Continuing ontology engineering for sewer rising main event detection Harmonising with standard units of measure ontologies User interface refinement, and user testing Very much a prototype / proof-of-concept Code generation module for deploying event constraints into GSN Performance and load testing to handle volumes of data Detecting sewer rising main events using an ontology-driven event processing system | Jonathan Yu 16 |

17. Extension idea - fusing real-time data with domain knowledge base Detecting sewer rising main events using an ontology-driven event processing system | Jonathan Yu 17 | Knowledge Base Sensor Network Real-time data Event of Interest Query knowledge base (domain knowledge) Notifications Populate knowledge base with parameterised historical pipe failure data. Infer likelihood of pipe failure based on physical attributes and known operating environment

18. Summary Value of analysing ‘business-as-usual’ data for early detection of sewer mains pipe failure Investigated a variety of timeseries analysis methods that is suitable for breakpoint detection of sewer rising mains failure events – SMA, Breakpoint analysis, Near real-time trend detection Does not require extensive training datasets Ontology-driven event detection system User interface for defining machine readable event constraints using domain- specific ontologies System for deploying these event constraints for detection over sensor network Detecting sewer rising main events using an ontology-driven event processing system | Jonathan Yu 18 |

19. Land and Water Scott Gould Research Projects Officer t +61 3 9252 6103 escott.gould@csiro.au wwww.csiro.au/clw ICT Centre Kerry Taylor Principal Research Scientist t+61 2 6216 7038 ekerry.taylor@csiro.au wwww.csiro.au/ict Land and Water Donavan Marney Research team leader t +61 3 9252 6585 edonavan.marney@csiro.au wwww.csiro.au/clw LAND AND WATER Thank you Land and Water Jonathan Yu Software Engineer t+61 3 9252 6440 ejonathan.yu@csiro.au wwww.csiro.au/clw Land and Water Paul Davis Research Scientist t +61 3 9252 6310 epaul.davis@csiro.au wwww.csiro.au/clw Land and Water Irina Emelyanova Research Scientist t +61 8 9333 6243 e Irina. Emelyanova @csiro.au wwww.csiro.au/clw

20. Advantages of semantics-based approach Transparent and transferrable Rules, vocabularies, mappings are captured in the ontologies Can deploy to other systems as long as they are mapped Traceable Alerts can attach metadata to describe triggers: what, why, when End users can focus on exploring real-time datasets Semantics-based approach for defining complex event rules for algal bloom detection | Jonathan Yu 20 | CurationCodingAnalysis, Monitoring, Management CurationCoding Analysis, Monitoring, Management

21. Detecting sewer rising main events using an ontology-driven event processing system | Jonathan Yu 21 | Ontology-driven event detection system 1. Composes CE Sensor Network Ontology-enabled User Interface Semantic Mediator GSN Ontologies SSN Ontology Domain Ontology 7.Updates UI with alert 3. Deploys CE to GSN as VSensor via translation capture rule to sensor API mapping capture sensor / data sources mappings 6. Matching event alert generated 2. Submits CE definition captures alerts captures CE definition 8. Views alert 5. Sensor streams data Users