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KwangSoo Yang, Shashi Shekhar Jing Dai, Sambit Sahu, and Milind Naphade Department of Computer Science, University of Minnesota IBM T.J. Watson Research.

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Presentation on theme: "KwangSoo Yang, Shashi Shekhar Jing Dai, Sambit Sahu, and Milind Naphade Department of Computer Science, University of Minnesota IBM T.J. Watson Research."— Presentation transcript:

1 KwangSoo Yang, Shashi Shekhar Jing Dai, Sambit Sahu, and Milind Naphade Department of Computer Science, University of Minnesota IBM 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. 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 Source: World Meteorological Organisation (WMO), Geneva, 1996; Global Environment Outlook 2000 (GEO), UNEP, Earthscan, London, 1999.

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

4 Hand grab sampling Current water management Water Theft Water sensors Crumbling infrastructure Leaks and theft in agriculture area Few sensors (home water meter and plant meter) Hidden water network (underground water and buried pipelines) 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 Source: IBM Smarter Water Management IBM Smarter Planet emphasizes smarter water management for planning, developing, distributing, and managing optimal use of limited water resources. A lot of data are needed to fully understand, model, and predict how water flows around the planet. SensingMetering Real Time Data Integration Real Time + Historical data Data Modeling + Analytics Visualization + Decisions Measuring, Monitoring, Modeling, and Managing

6 Spatio-Temporal Network (STN) Water network flow is represented and analyzed as spatio-temporal network datasets. Minnesota river network source: http://geology.com/ 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 Ground water network source: http://me.water.usgs.gov

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

8 Hidden STN Water Cooling System / Nuclear reactor source: http://www.firstpr.com.au/jncrisis/ Water Network Tomography The internal structure and status of the cooling system may not be directly observable due to radioactive emissions from the damaged nuclear reactors. 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. water loss flow delay or bottleneck topology/connectivity Given end-to-end measurements infer identify monitor Example) Water Cooling System

9 Hidden STN Water Network Tomography X1X2 X3X4 Y1 Y2 Y3Y4 Y1 Y2 Y3 Y4 = X1 X2 X3 X4 1 0 1 0 1 0 0 0 0 1 1 0 0 1 0 1 Y = A X Input: Given nodes X and Y, flows at nodes Y Output: Estimate A Objective: Minimize the error Constraints: Incorporate the temporal and spatial dependence : Sparse matrix : Tracer data Origin-destination (OD) water network flow OD matrix contains several components, including origin, destination, and barrier information (e.g., max flow, amount of resistance, speed, direction) Ex) Cost matrix Flow matrix Connectivity matrix Hardness under-constrained under-determined N measured data N 2 hidden data

10 STN Non-stationarity Water network supplydemand Climate changeEventGeography DroughtContaminationPopulation TemperatureLeakageFactories/Farms PrecipitationDisasterPools Water supply and consumption patterns change over time Time varying factors Flow distribution in water networks is not i.i.d. 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. Temporal information ex) hot moments and weather change Water networks ex) sink, branch points, and water mains Geographical information ex) home address and elevation Historical and real-time datasets Inappropriate pressure levels and flows High risk areas in networks Burst/corroded pipes Example) Water leakage detection system Monitoring Detecting anomalies

12 Complexity of STN datasets 123 4 5 6789 10 11 12 7 12 34 5 6 8 9 1011 12 13 14 15 16 Inflowoutflow seismogenic rupture Pipes can be damaged which reduces the flow rates Inspections of individual network components such as buried pipes are often impractical due to exceedingly large costs and time. Network flow Pressure Transmission time OD matrix Cost Matrix Flow Matrix Structure Matrix Monitoring data Event data Spatial Location Temporal property Detect the water leakage Recover the damaged pipe line The challenge here is that the anomaly detection algorithm would be intractable due to the size of the spatio-temporal network datasets. Ex) earthquake event

13 Water Quality Index : 40 (bad) – 0 (good) Lagrangian reference frame  Eulerian : stationary point  Lagrangian : moving point Measurement methods Moving sensor 20 1234Time Water quality index 3040 20 12345678Time Water quality index 20 910 20 12345678Time Water quality index 40204020 402040 910 Environmental Forensics : Where did contaminant come from ? : What are hotspots and hot moments ? Introduction to Environmental Forensics Brian L. Murphy, Robert D. Morrison

14 Lagrangian reference frame (Source: http://www.sfgate.com/cgi-bin/news/oilspill/busan) 0 12345678Time 000000000 910 0 12345678Time 900 000000 910 0 12345678Time 0900 00000 910 0 12345678Time 000000000 910 Access of STN datasets requires a Lagrangian frame of reference which coordinates STN datasets with STN connectivity.

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

16 General frameworks to analyze STN datasets Source: www.crisiscommunication.fi/files/download/ForOnline_NokiaCase.pd f Example) Nokia water supply contamination - 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. Water supply network Ground water network River Network Sewerage network Heterogeneous multi-modal networks Population map STN data integration problem - Heterogeneous multi-modal networks - Time-varying properties - Correlation properties

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.: Jc glenn, tj gordon 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 Source: International Water Management Institute

21 Spatio-Temporal Network (STN) Water network flow is represented and analyzed as spatio-temporal network datasets. Minnesota river network source: http://geology.com/ 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 Ground water network source: http://me.water.usgs.gov

22 Time varying water network tomography 1. Hidden STN X1 Y3 t1t2t3t4 X1 Y3 Time Time dependent network flow 323 X1(t)Y3(t) A(t) (?) Issues : Time varying water flows Dependency of each nodes Uneven distribution of flows Data quality: missing, uncertain, unstable sample rate Computationally expensive How can we estimate network topology & properties ?

23 closed open closed open Water Pipelines Flow directionPressure Flow rate


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