Abstract
Along with the rapid development of IoT (Internet of Things), there comes the ‘Big Data’ era with the fast growth of digital data and the requirements rise for gaining useful knowledge by analyzing the rich data of complex types. How to effectively and efficiently apply data mining techniques to analyze the big data plays a crucial role in real-world use cases. In this paper, we propose a novel complex-events analytical system based on episode pattern mining techniques. The proposed system consists of four major components, including data preprocessing, pattern mining, rules management and prediction modules. For the core mining process, we proposed a new algorithm named EM-CES (Episode Mining over Complex Event Sequences) based on the sliding window approach. We also make the proposed system integrable with other application platform for complex event analysis, such that users can easily and quickly make use of it to gain the valuable information from complex data. Finally, excellent experimental results on a real-life dataset for electric power consumption monitoring validate the efficiency and effectiveness of the proposed system.
This study is conducted under the “Advanced Sensing Platform and Green Energy Application Technology Project” of the Institute for Information Industry which is subsidized by the Ministry of Economy Affairs of the Republic of China.
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Aboalsamh, H.A., Hafez, A.M., Assassa, G.M.R.: An efficient stream mining technique. WSEAS Trans. Inf. Sci. Appl. 5(7), 1272–1281 (2008)
Casas-Garriga, G.: Discovering unbounded episodes in sequential data. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 83–94. Springer, Heidelberg (2003)
Fournier-Viger, P., Nkambou, R., Tseng, V.S.: RuleGrowth: mining sequential rules common to several sequences by pattern-growth. In: ACM Symposium on Applied Computing, pp. 956–961, Taiwan (2011)
Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 2–11 (2003)
Lin, Y., Huang, C., Tseng, V.S.: A novel mining methodology for stock investment. J. Inf. Sci. Eng. 30, 571–585 (2014)
Lin, Y., Jiang, P., Tseng, V.S.: Efficient mining of frequent target episodes from complex event sequences. In: Proceedings of International Computer Symposium (2014)
Ma, X., Pang, H., Tan, K.L.: Finding constrained frequent episodes using minimal occurrences. In: Proceedings of IEEE International Conference on Data Mining, pp. 471–474 (2004)
Mallik, R., Kargupta, H.: A sustainable approach for demand prediction in smart grids using a distributed local asynchronous algorithm. In: Proceedings of CIDU Conference of Intelligent Data Understanding, pp. 01–15 (2011)
Mannila, H., Toivonen, H., Verkamo, A.I.: Discovering frequent episodes in sequences. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 210–215 (1995)
Mannila, H., Toivonen, H., Verkamo, A.I.: Discovering frequent episodes in sequences. Data Min. Knowl. Disc. 1(3), 259–289 (1997)
Mannila, H., Toivonen, H.: Discovering generalized episodes using minimal occurrences. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 146–151 (1996)
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings of International Conference on Data Engineering, pp. 215–226 (2001)
Tseng, V.S., Lee, C.-H.: Effective temporal data classification by integrating sequential pattern mining and probabilistic induction. Expert Syst. Appl. (ESWA) 36(5), 9524–9532 (2009)
Wu, C.-W., Lin, Y.-F., Yu, P.S., Tseng, V.S.: Mining high utility episodes in complex event sequences. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 536–544 (2013)
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Tseng, J.C.C., Gu, JY., Wang, P.F., Chen, CY., Tseng, V.S. (2015). A Novel Complex-Events Analytical System Using Episode Pattern Mining Techniques. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_48
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DOI: https://doi.org/10.1007/978-3-319-23862-3_48
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