Abstract
In this paper we present a novel method to mine the correlations of events in sensor networks to extract correlation patterns of sensors’ behaviors by using an unsupervised algorithm based on a hash table. The goal is to discover anomalous events in a large sensor network where its structure is unknown. Our algorithm enables users to select the correlation confidence level and only display the significant event correlations. Our experiment results show that it can discover significant event correlations in both continuous and discrete signals from heterogeneous sensor networks. The applications include smart building design and large network data mining.
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Mahoney, M.V., Chan, K.: Trajectory Boundary Modeling of Time Series for Anomaly Detection. SIGKDD Explorations Newsletter 7(2), 132–136 (2005)
Fabian, M.: Unsupervised Pattern Mining from Symbolic Temporal Data. SIGKDD Explorations Newsletter 9(1), 41–45 (2007)
Zhang, T., Yue, D., Gu, Y., Yu, G.: Boolean Representation Based Data-Adaptive Correlation Analysis over Time Series Streams. In: CIKM 2007 Information and knowledge management, pp. 203–212. ACM, New York (2007)
Ke, Y., Cheng, J.: Correlated Pattern Minging in Quantitative Databases. ACM Transactions on Database Systems 3(33), Article 14 (August 2008)
Ke, Y., Cheng, J.: Correlation search in graph databases. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 390–399. ACM, New York (2007)
Cohen, E., Datar, M.: Finding Interesting Associations without Support Pruning. IEEE Transaction on Knowledge and Data Engineering 1(13) (February 2001)
Tsai, K.-C., Sung, J.-T.: An Environment Sensor Fusion Application on Smart Building Skins. In: IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing, June 2008, pp. 291–295 (2008)
Sharples, S., Callaghan, V., Clarke, G.: A Multi-agent Architecture for Intelligent Building Sensing and Control. International Sensor Review Journal, 1–8 ( May 1999)
Hagras, H., Callaghan, V., Colley, M., Clarke, G.: A Hierarchical Fuzzy–genetic Multiagent Architecture for Intelligent Buildings Online Learning, Adaptation and Control. Information Sciences 150, 33–57 (2003)
Kay, R.: Discovery of Frequent Distributed Event patterns in Sensor Networks. In: Verdone, R. (ed.) EWSN 2008. LNCS, vol. 4913, pp. 106–124. Springer, Heidelberg (2008)
Boukerche, A., Samarah, S.: A Novel Algorithm for Mining Association Rules in Wireless Ad Hoc Sensor Networks. IEEE Transactions on Parallel and Distributed Systems 19(7) (July 2008)
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rule. In: Proc. 20th Int’l Conf. Very Large Data Bases (VLDB 1994), pp. 487–499. Morgan Kaufrmann, San Francisco (1994)
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the 11th international Conference on Data Engineering, pp. 3–14. IEEE Press, Los Alamitos (1995)
Cong, S., Han, J.: Parallel mining of closed sequential patterns. In: KDD 2005: Eleventh ACM SIGKDD international conference on knowledge discovery in data mining, Chicago, Illinois, pp. 562–567 (2005)
Intel Lab Data (2007), http://berkeley.intel-research.net/labdata/
Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach. In: Data Mining and Knowledge Discovery, 2000 ACM SIGMOD international conference on Management of data, vol. 8(1), pp. 1–12 (2000)
Zaki, M., Hsiao, C.: Charm: An Efficient Algorithm for Closed Itemset Mining. In: SDM 2002 (April 2002)
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Ni, P., Wan, L., Cai, Y. (2009). Event Correlations in Sensor Networks. In: Allen, G., Nabrzyski, J., Seidel, E., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2009. ICCS 2009. Lecture Notes in Computer Science, vol 5545. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01973-9_56
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DOI: https://doi.org/10.1007/978-3-642-01973-9_56
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