Smarter Water Management: A Challenge for Spatio -Temporal Network Databases (Vision Paper)

1. Smarter Water Management: A Challenge for Spatio-Temporal Network Databases(Vision Paper) KwangSoo Yang, ShashiShekharJing Dai, SambitSahu, and MilindNaphadeDepartment of Computer Science, University of MinnesotaIBM T.J. Watson Research Hawthorne

2. The importance of water Water is one of our most important natural resources and water scarcity may be the most underestimated resource issue facing the world today. Source: World Meteorological Organisation (WMO), Geneva, 1996; Global Environment Outlook 2000 (GEO), UNEP, Earthscan, London, 1999. By 2025 about 3 billion people could face water scarcity due to the climate change, population growth, and increasing demand for water per capita. - United Nations, The Millennium Project

3. Water scarcity Distribution of precipitation is varying in space and time. High rate of evaporation from surface water resource Water quality - Leakage of saline or contaminated water from the land surface- Water contamination by sewage effluent. Example) Water Pollution Water scarcity is not only an issue of enough water but also of access to safe water. Developing intelligent water resource management systems is necessary to remedy the problem.

4. Current water management Hand grab sampling • Crumbling infrastructure • Leaks and theft in agriculture area • Few sensors (home water meter and plant meter) • Hidden water network (underground water and buried pipelines) Water Theft Water sensors One technology to remedy this problem is to implement fully integrated systems which allow monitoring, analyzing, controlling, and optimizing of all aspects of water flows.

5. Smarter Water Management IBM Smarter Planet emphasizes smarter water management for planning, developing, distributing, and managing optimal use of limited water resources. Measuring, Monitoring, Modeling, and Managing Sensing Metering Real TimeData Integration Real Time+ Historical data Source: IBM Smarter Water Management Data Modeling+ Analytics Visualization + Decisions A lot of data are needed to fully understand, model, and predict how water flows around the planet.

6. Spatio-Temporal Network (STN) Water network flow is represented and analyzed as spatio-temporal network datasets. Minnesota river networksource: http://geology.com/ Ground water networksource: http://me.water.usgs.gov Water pipe network source: Wikipedia Spatio-temporal network databases (STNDB) will likely be a key component of smarter water management since effectiveness of decision depends on the quality of information

7. Challenges for STN of water networks The key issue is the quality of dataset to fully understand, model, and predict water flows in the network. 1. STN datasets not fully observable - underground natural flows - buried pipes may shift 2. Assess of STN datasets requires a novel semantics. ex) Lagrangian reference frame 3. Heterogeneity of real-world STN datasets

8. Hidden STN Water Network Tomography Example) Water Cooling System The internal structure and status of the cooling system may not be directly observable due to radioactive emissions from the damaged nuclear reactors. Water Cooling System / Nuclear reactorsource: http://www.firstpr.com.au/jncrisis/ Network tomography is one solution to understand the internal characteristic of the network using end-to-end measurements, without needing the cooperation of internal nodes. infer topology/connectivity Given end-to-end measurements flow delay or bottleneck identify water loss monitor

9. Hidden STN Water Network Tomography Y1 X1 X2 = Y1 0 0 1 1 X1 Y2 1 1 0 0 X2 Origin-destination (OD) water network flow Y2 X3 X4 Y3 0 1 1 0 X3 Y4 0 0 0 1 X4 Input : Given nodes X and Y, flows at nodes Y Y3 Ex) Cost matrix Flow matrix Connectivity matrix Y4 Y = A X Output : Estimate A Objective : Minimize the error Constraints : Incorporate the temporal and spatial dependence: Sparse matrix : Tracer data OD matrix contains several components, including origin, destination, and barrier information (e.g., max flow, amount of resistance, speed, direction) N measured data N2 hidden data Hardness • under-constrained • under-determined

10. STN Non-stationarity Water supply and consumption patterns change over time Water network supply demand Flow distribution in water networks is not i.i.d. Time varying factors Ex) Time varying water Consumption

11. STN Non-stationarity Spatial location + network connectivity + time-varying property Datasets grows massively while the density of the datasets becomes sparse. Example) Water leakage detection system Historical and real-time datasets • Inappropriate pressure levels and flows • High risk areas in networks • Burst/corroded pipes Water networksex) sink, branch points, and water mains Monitoring Geographical information ex) home address and elevation Detectinganomalies Temporal informationex) hot moments and weather change

12. Complexity of STN datasets Ex) earthquake event Inflow outflow Pipes can be damaged which reduces the flow rates 1 2 3 1 2 3 4 Inspections of individual network components such as buried pipes are often impractical due to exceedingly large costs and time. 5 5 4 6 7 8 Monitoring data 6 7 8 9 Network flowPressureTransmission time 10 11 9 13 14 12 11 12 10 OD matrix 15 seismogenicrupture 16 Detect the water leakageRecover the damaged pipe line Cost Matrix Flow Matrix Structure Matrix outflow outflow Event data Spatial Location Temporal property The challenge here is that the anomaly detection algorithm would be intractable due to the size of the spatio-temporal network datasets.

13. Lagrangian reference frame Measurement methods Water Quality Index : 40 (bad) – 0 (good) • Eulerian : stationary point • Lagrangian : moving point Moving sensor Time 1 2 3 4 Water quality index 20 30 40 40 Time Time 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 10 Water quality index Water quality index 20 20 40 20 20 20 40 20 20 20 20 20 40 20 20 20 20 40 40 20 Environmental Forensics : Where did contaminant come from ? : What are hotspots and hot moments ? Introduction to Environmental ForensicsBrian L. Murphy, Robert D. Morrison

14. Lagrangian reference frame Access of STN datasets requires a Lagrangian frame of reference which coordinates STN datasets with STN connectivity. Time Time Time Time 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5 6 6 6 6 7 7 7 7 8 8 8 8 9 9 9 9 10 10 10 10 0 0 0 0 0 90 0 0 90 0 0 0 90 0 0 0 0 0 90 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 (Source: http://www.sfgate.com/cgi-bin/news/oilspill/busan)

15. Lagrangian reference frame Need new STN database systems Example) A moving fluid (ABD) STN database systems Snapshot model Data types Storage models Access Methods Indexes Time expanded graph Queries

16. General frameworks to analyze STN datasets • STN models for complex real-world networks • Novel network analysis models Mathematical approach - Real-world network model is unknown or too complex to be mathematically described. Heterogeneous multi-modal networks Example) Nokia water supply contamination Water supply network Ground water network River Network Source: www.crisiscommunication.fi/files/download/ForOnline_NokiaCase.pdf Sewerage network STN data integration problem - Heterogeneous multi-modal networks - Time-varying properties - Correlation properties Population map

17. Conclusion Water resource management is one of most important part for our survival. We need a Spatio-temporal network databases and analysis tools to monitoring, analyzing, controlling, and optimizing of all aspects of water flows. We poses 3 main challenges to handle spatio-temporal network datasets for water flows.

18. Acknowledgement This work was supported by NSF and USDOD.

19. References 1. Water: How can everyone have sufficient clean water without conflict?(2010), United Nations, http://goo.gl/pdsr0, Retrieved Mar 17, 2011. 2. Smarter planet, Wikipedia, http://goo.gl/ay5W8, Retrieved Mar 17, 2011. 3. Water Management, Wikipedia, http://goo.gl/aNllg, Retrieved Mar 17, 2011. 4. Let’s Build a Smarter Planet: Smarter Water Management, Dr. Cameron Brooks, Sep 22, 2010, IBM, http://goo.gl/XvCIB 5. IBM Smarter Water Keynote(2010), IBM, http://goo.gl/4zRMw 6. Water supply network, Wikipedia, http://goo.gl/cF4IG, Retrieved Mar 17, 2011. 7. Nokia water supply contamination, Wikipedia, http://goo.gl/fDfVx, Retrieved May 30, 2011. 8. Batchelor, G.: An introduction to fluid dynamics. Cambridge Univ Pr (2000) 9. Chartres, C., Varma, S.: Out of Water: From Abundance to Scarcity and How to Solve the World’s Water Problems. Ft Pr (2010) 10. Chen, F., et al.: Activity analysis based on low sample rate smart meters. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining(to appear). ACM (2011) 11. Dai, J., Chen, F., Sahu, S., Naphade, M.: Regional behavior change detection via local spatial scan. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. pp. 490–493. ACM (2010) 12. Marien, M.: Jcglenn, tjgordon and e. florescu, 2010 state of the future, the millennium project, washington, http://www.stateofthefuture.org. Futures (2010) 13. Molden, D.: Water for food, water for life: a comprehensive assessment of water management in agriculture. Earthscan/James & James (2007) 14. Perry, W.: Grand challenges for engineering. Engineering (2008) 15. Vardi, Y.: Network Tomography: Estimating Source-Destination Traffic Intensities from Link Data. Journal of the American Statistical Association 91(433) (1996)

20. Water Pipelines open closed open closed Flow direction Pressure Flow rate