Abstract
We address the problem of event classification for proactive fiber break detection in high-speed optical communication systems. The proposed approach is based on monitoring the State of Polarization (SOP) via digital signal processing in a coherent receiver. We describe in details the design of a classifier providing interpretable decision rules and enabling low-complexity real-time detection embedded in network elements. The proposed method operates on SOP time series, which define trajectories on the 3D sphere; SOP time series are low-pass filtered (to reduce measurement noise), pre-rotated (to provide invariance to the starting point of trajectories) and converted to quaternion domain. Then quaternion sequences are recoded to relational data for automatic variable construction and selection. We show that a naïve Bayes classifier using a limited subset of variables can achieve an event classification accuracy of more than 99% for the tested conditions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Invariance to the starting point is quite different from invariance to time scale that could be addressed using dynamic time warping (DTW). Here DTW would not solve the problem of invariance to the starting point.
- 2.
Up to now this is an adhoc decision discussed in the last section of this paper.
References
Adewuyi, A.P., Wu, Z., Serker, N.K.: Assessment of vibration-based damage identification methods using displacement and distributed strain measurements. Struct. Health Monit. 8(6), 443–461 (2009)
Bagnall, A., Lines, J., Hills, J., Bostrom, A.: Time-series classification with COTE: the collective of transformation-based ensembles. IEEE Trans. Knowl. Data Eng. 27(9), 2522–2535 (2015)
Bagnall, A., Davis, L., Hills, J., Lines, J.: Transformation based ensembles for time series classification. In: Proceedings of the 12th SDM, April 2012
Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Disc. 31(3), 606–660 (2017)
Boitier, F., et al.: Proactive fiber damage detection in real-time coherent receiver. In: Proceedings of the ECOC (2017)
Boitier, F., et al.: Seamless optical path restoration with just-in-time resource allocation leveraging machine learning. In: Proceeding of the ECOC, Demo Session (2018)
Boullé, M.: A grouping method for categorical attributes having very large number of values. In: Perner, P., Imiya, A. (eds.) MLDM 2005. LNCS (LNAI), vol. 3587, pp. 228–242. Springer, Heidelberg (2005). https://doi.org/10.1007/11510888_23
Boullé, M.: MODL: a Bayes optimal discretization method for continuous attributes. Mach. Learn. 65(1), 131–165 (2006)
Boullé, M.: Compression-based averaging of selective naive Bayes classifiers. J. Mach. Learn. Res. 8, 1659–1685 (2007)
Boullé, M.: Tagging fireworkers activities from body sensors under distribution drift. In: Proceedings of Federated Conference on Computer Science and Information System, pp. 389–396 (2015)
Boullé, M.: Khiops: outil d’apprentissage supervisé automatique pour la fouille de grandes bases de données multi-tables. In: Extraction et Gestion des Connaissances, pp. 505–510 (2016). http://www.khiops.com
Boullé, M.: Predicting dangerous seismic events in coal mines under distribution drift. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Proceedings of Federated Conference on Computer Science and Information System, pp. 221–224 (2016)
Boullé, M., Charnay, C., Lachiche, N.: A scalable robust and automatic propositionalization approach for Bayesian classification of large mixed numerical and categorical data. Mach. Learn. 108, 229–266 (2018)
Casteljau, P.D.: Les quaternions. Dunod, Paris (1987)
Chen, Y., et al.: The UCR time series classification archive, July 2015. www.cs.ucr.edu/~eamonn/time_series_data/
Dutisseuil, E., et al.: 34 Gb/s PDM-QPSK coherent receiver using SiGe ADCs and a single FPGA for digital signal processing. In: Proceedings of the OFC, p. OM3H.7 (2012)
Džeroski, S.: Relational data mining. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 887–911. Springer, Boston (2009). https://doi.org/10.1007/978-0-387-09823-4_46
Dzeroski, S., Lavrac, N.: Inductive Logic Programming: Techniques and Applications. Prentice Hall, New York (1994)
Fawcett, T.: ROC graphs: notes and practical considerations for researchers. Technical Report HPL-2003-4, HP Laboratories (2004)
Gay, D., Guigourés, R., Boullé, M., Clérot, F.: Feature extraction over multiple representations for time series classification. In: International Workshop NFMCP held at ECML/PKDD, pp. 18–34 (2013)
Hamilton, W.R.: On a new species of imaginary quantities connected with a theory of quaternions. Proc. R. Ir. Acad. 2, 424–434 (1843)
Hanson, A.J.: Visualizing Quaternions. Morgan Kaufmann Publishers, Burlington (2006)
Hauske, F.N., Kuschnerov, M., Spinnler, B., Lankl, B.: Optical performance monitoring in digital coherent receivers. J. Lightwave Technol. 27(16), 3623–3631 (2009)
Hayford-Acquah, T., Asante, B.: Causes of fiber cut and the recommendation to solve the problem. IOSR J. Electron. Commun. Eng. 12, 46–64 (2017)
Hills, J., Lines, J., Baranauskas, E., Mapp, J., Bagnall, A.: Classification of time series by shapelet transformation. Data Min. Knowl. Disc. 28(4), 851–881 (2014)
Kikuchi, K.: Fundamentals of coherent optical fiber communications. J. Lightwave Technol. 34(1), 157–179 (2016)
Krogel, M.-A., Rawles, S., Železný, F., Flach, P.A., Lavrač, N., Wrobel, S.: Comparative evaluation of approaches to propositionalization. In: Horváth, T., Yamamoto, A. (eds.) ILP 2003. LNCS (LNAI), vol. 2835, pp. 197–214. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39917-9_14
Krogel, M.-A., Wrobel, S.: Transformation-based learning using multirelational aggregation. In: Rouveirol, C., Sebag, M. (eds.) ILP 2001. LNCS (LNAI), vol. 2157, pp. 142–155. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44797-0_12
Lachiche, N.: Propositionalization, pp. 812–817. Springer, Boston (2010). https://doi.org/10.1007/978-0-387-30164-8
Langley, P., Iba, W., Thompson, K.: An analysis of Bayesian classifiers. In: Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI 1992), pp. 223–228 (1992)
Lavrač, N., Železný, F., Flach, P.A.: RSD: relational subgroup discovery through first-order feature construction. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, pp. 149–165. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36468-4_10
Layec, P., Dupas, A., Verchère, D., Sparks, K., Bigo, S.: Will metro networks be the playground for (true) elastic optical networks? J. Lightwave Technol. 35(6), 1260–1266 (2017)
Lemaire, V., Salperwyck, C., Bondu, A.: A survey on supervised classification on data streams. In: Zimányi, E., Kutsche, R.-D. (eds.) eBISS 2014. LNBIP, vol. 205, pp. 88–125. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-17551-5_4
Lines, J., Bagnall, A.: Time series classification with ensembles of elastic distance measures. Data Min. Knowl. Disc. 29(3), 565–592 (2015)
Liu, X., Jin, B., Bai, Q., Wang, D., Wang, Y.: Distributed fiber-optic sensors for vibration detection. Sensors 16, 1164 (2016)
Nokia white paper: Advances in optical layer restoration (2017). https://www.nokia.com/blog/optical-layer-restoration-improving-efficiency/
Pesic, J., Le Rouzic, E., Brochier, N., Dupont, L.: Proactive restoration of optical links based on the classification of events. In: Proceedings of the ONDM, pp. 1–6 (2011)
Pesic, J., Meuric, J., Le Rouzic, E., Dupont, L., Morvan, M.: Proactive failure detection for WDM carrying IP. In: Proceedings of IEEE INFOCOM, pp. 2971–2975 (2012)
Pesic, J.: Study of the mechanisms associated with the preventive network restoration in fiber optic core networks. Ph.D. thesis, Université de Bretagne-Sud (2012)
Project SENDATE-Tandem: Secure networking for a data center cloud in Europe - tailored network for data centers in the metro. https://www.celticnext.eu/project-sendate-tandem/
Rodríguez, J.J., Alonso, C.J., Boström, H.: Learning first order logic time series classifiers: rules and boosting. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 299–308. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45372-5_29
Schäfer, P., Leser, U.: Fast and accurate time series classification with WEASEL. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 637–646 (2017)
Shoemake, K.: Animating rotation with quaternion curves. ACM SIGGRAPH Comput. Graph. 19(3), 245–254 (1985)
Simsarian, J.E., Winzer, P.J.: Shake before break: per-span fiber sensing with in-line polarization monitoring. In: Proceedings of OFC, p. M2E.6 (2017)
Time-series classification challenge: Workshop advanced analytics and learning on temporal data at ECML (2016). https://aaltd16.irisa.fr/challenge/
Warren Liao, T.: Clustering of time series data - a survey. Pattern Recognit. 38(11), 1857–1874 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Lemaire, V., Boitier, F., Pesic, J., Bondu, A., Ragot, S., Clérot, F. (2020). Proactive Fiber Break Detection Based on Quaternion Time Series and Automatic Variable Selection from Relational Data. In: Lemaire, V., Malinowski, S., Bagnall, A., Bondu, A., Guyet, T., Tavenard, R. (eds) Advanced Analytics and Learning on Temporal Data. AALTD 2019. Lecture Notes in Computer Science(), vol 11986. Springer, Cham. https://doi.org/10.1007/978-3-030-39098-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-39098-3_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-39097-6
Online ISBN: 978-3-030-39098-3
eBook Packages: Computer ScienceComputer Science (R0)