Abstract
In an industrial context application aiming at performing aeronautic maintenance tasks scheduling, we propose a frequent Interval Time Sequences (ITS) extraction technique from discrete temporal sequences using a sliding window approach to relax time constraints. The extracted sequences offer an interesting overview of the original data by allowing a temporal leeway on the extraction process. We formalize the ITS extraction under classical time and support constraints and conduct some experiments on synthetic data to validate our proposal.
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References
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceeding of ICDE Conference, Taipei, Taiwan, pp. 3–15. IEEE Computer Society Press (1995)
Agrawal, R., Srikant, R.: Mining sequential patterns: Generalizations and performance improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 1–17. Springer, Heidelberg (1996)
Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proc. 20th Int. Conf. Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)
Allen, J.F.: Maintaining knowledge about temporal intervals. Communications of ACM 26 (1983)
Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, Canada, pp. 429–435. ACM (2002)
Ceci, M., Loglisci, C., Salvemini, E., D’Elia, D., Malerba, D.: Mining spatial association rules for composite motif discovery. In: Mathematical Approaches to Polymer Sequence Analysis and Related Problems, pp. 87–109 (2011)
Fournier-Viger, P., Nkambou, R., Nguifo, E.M.: A knowledge discovery framework for learning task models from user interactions in intelligent tutoring systems. In: Gelbukh, A., Morales, E.F. (eds.) MICAI 2008. LNCS (LNAI), vol. 5317, pp. 765–778. Springer, Heidelberg (2008)
Giannotti, F., Nanni, M., Pedreschi, D., Pinelli, F.: Mining sequences with temporal annotations. In: Proceedings of the 2006 ACM Symposium on Applied Computing (SAC), pp. 593–597. ACM (2006)
Guyet, T., Quiniou, R.: Mining temporal patterns with quantitative intervals. In: Workshops Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), Pisa, Italy, pp. 218–227. IEEE Computer Society (2008)
Guyet, T., Quiniou, R.: Extracting temporal patterns from interval-based sequences. In: IJCAI, Barcelona, Catalonia, Spain, pp. 1306–1311 (2011)
Hirate, Y., Yamana, H.: Generalized sequential pattern mining with item intervals. JCP 1(3), 51–60 (2006)
Li, C., Yang, Q., Wang, J., Li, M.: Efficient mining of gap-constrained subsequences and its various applications. ACM Trans. Knowl. Discov. Data 6(1), 2:1–2:39 (2012)
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: Prefixspan: Mining sequential patterns by prefix-projected growth. In: Proceedings of the 17th International Conference on Data Engineering ICDE, pp. 215–224 (2001)
Pham, Q., Raschia, G., Mouaddib, N., Saint-Paul, R., Benatallah, B.: Time sequence summarization to scale up chronology-dependent applications. In: EDBT 2008, 11th International Conference on Extending Database Technology, Hong Kong, China, pp. 1137–1146 (2009)
Plantevit, M., Laurent, A., Teisseire, M., et al.: Extraction de motifs séquentiels multidimensionnels clos sans gestion d’ensemble de candidats. In: EGC 2007: Extraction et Gestion des Connaissances, p. 6 (2007)
Rabatel, J., Bringay, S., Poncelet, P.: So_mad: Sensor mining for anomaly detection in railway data. In: Perner, P. (ed.) ICDM 2009. LNCS (LNAI), vol. 5633, pp. 191–205. Springer, Heidelberg (2009)
Srinivasulu, P., Rao, J.R., Babu, I.R.: Network intrusion detection using fp tree rules. CoRR, abs/1006.2689 (2010)
Wu, S., Chen, Y.: Mining nonambiguous temporal patterns for interval-based events. IEEE Trans. on Knowl. and Data Eng. 19, 742–758 (2007)
Yi-Cheng, C., Ji-Chiang, J., Wen-Chih, P., Suh-Yin, L.: An efficient algorithm for mining time interval-based patterns in large database. In: ACM, Proceedings of CIKM Conference, Hong Kong, China, pp. 49–58 (2010)
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Ben Zakour, A., Maabout, S., Mosbah, M., Sistiaga, M. (2014). Relaxing Time Granularity for Mining Frequent Sequences. In: Guillet, F., Pinaud, B., Venturini, G., Zighed, D. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-02999-3_4
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DOI: https://doi.org/10.1007/978-3-319-02999-3_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02998-6
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