Online Detecting Spreading Events with the Spatio-temporal Relationship in Water Distribution Networks
In a water distribution network, massive streams come from multiple sensors concurrently. In this paper, we focus on detecting abnormal events spreading among streams in real time. The event is defined as a combination of multiple outliers caused by one same mechanism and once it breaks out, it will spread out in networks. Detecting these spreading events timely is an important and urgent problem both in research community and for public health. To the best of our knowledge, few methods for discovering abnormal spreading events in networks are proposed. In this paper, we propose an online method based on the spatial and temporal relationship among the streams. Firstly we utilize Bayesian Network to model the spatial relationship among the streams, and a succinct data structure to model the temporal relationship within a stream. Then we select some nodes as seeds to monitor and avoid monitoring all sensor streams, thus improving the response speed during detection. The effectiveness and strength of our method is validated by experiments on a real water distribution network.
KeywordsSpatio-temporal relationship event detection Bayesian Network
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- 1.Hall, J., Zaffiro, A.D., Marx, R.B., Kefauver, P.C., Krishnan, E.R., Herrmann, J.G.: Online water quality parameters as indicators of distribution system contamination. Journal American Water Works Association 99(1), 66–77 (2007)Google Scholar
- 2.Hart, D.B., Klise, K.A., McKenna, S.A., Wilson, M.P.: CANARY User’s Manual Version4.1. Sandia National Laboratories. U.S. Environmental Protection Agency (2009)Google Scholar
- 3.Ihler, A., Hutchins, J., Smyth, P.: Adaptive event detection with time-varying poisson processes. In: Proceedings of the 12th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New York, NY, USA, pp. 207–216 (2006)Google Scholar
- 5.Keogh, E., Lonardi, S., Yuan-chi Chiu, B.: Finding surprising pat-terns in a time series database in linear time and space. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, July 23-26 (2002)Google Scholar
- 9.Rossman, L.A.: EPANET2 user’s manual. National Risk Management Re-search Laboratory: U.S. Environmental Protection Agency (2000)Google Scholar
- 10.Ostfeld, A., Uber, J.G., Salomons, E.: Battle of water sensor networks: A de-sign challenge for engineers and algorithms. In: WDSA (2006)Google Scholar
- 11.Franke, C., Gertz, M.: Detection and Exploration of Outlier Regions in Sensor Data Streams. In: SSTDM 2008: Workshop on Spatial and Spatiotemporal Data Min-ing at IEEE ICDM (2008)Google Scholar
- 12.Liu, W., Zheng, Y., Chawla, S., Yuan, J.: Discovering Spatio-Temporal Causal Interactions in Traffic Data Streams. In: 17th ACM SIGKOD Conference on Knowl-edge Discovery and Data Mining. ACM, San Diego (2011)Google Scholar
- 13.Burdakis, S., Deligiannakis, A.: Detecting Outliers in Sensor Networks using the Geometric Approach. In: Proc. of the International Conference on Data Engineering (2012)Google Scholar
- 14.Janakiram, D., Mallikarjuna, A., Reddy, V., Kumar, P.: Outlier Detection in Wireless Sensor Networks using Bayesian Belief Networks. In: Proc. IEEE Comsware (2006)Google Scholar