Abstract
The internet has greatly revolutionized the communication and has undoubtedly affects our everyday life from work to entertainment. In order to uphold the quality of network service, Communication Service Providers (CSPs) are striving to keep network service faults to a minimum. To achieve this, they need to detect early of any potential network problems and resolve service incidents promptly before customers are impacted. However, to train a supervised learning algorithm to automatically detect service disruptions, the training data needs to be labeled. It is certainly costly and time consuming process to rely on domain experts to annotate the data. This paper addresses the data annotation problem based on temporal interval relations. We evaluated our method on real-world data and compared it with baseline method.
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References
Ho, L.L., Cavuto, D.J., Hasan, M.Z., Feather, F.E., Papavassiliou, S., Zawadzki, A.G.: Adaptive network/service fault detection in transaction-oriented wide area networks. In: Proceedings of the Sixth IFIP/IEEE International Symposium on Integrated Network Management, 1999, Distributed Management for the Networked Millennium, pp. 761–775. IEEE (1999)
Glennan, T., Leckie, C., Erfani, S.M.: Improved classification of known and unknown network traffic flows using semi-supervised machine learning. In: Liu, J.K., Steinfeld, R. (eds.) ACISP 2016. LNCS, vol. 9723, pp. 493–501. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40367-0_33
Alonso, O.: Challenges with label quality for supervised learning. J. Data Inf. Qual. (JDIQ) 6(1), 2 (2015)
Hu, H., Kantardzic, M.M., Sethi, T.S.: Selecting samples for labeling in unbalanced streaming data environments. In: 2013 XXIV International Symposium on Information, Communication and Automation Technologies (ICAT), pp.1–7. IEEE (2013)
Erdmann, M., Ward, E., Ikeda, K., Hattori, G., Ono, C., Takishima, Y.: Automatic labeling of training data for collecting tweets for ambiguous tv program titles. In: 2013 International Conference on Social Computing (SocialCom), pp. 796–802. IEEE (2013)
Mostafazadeh, N., Grealish, A., Chambers, N., Allen, J., Vanderwende, L.: CaTeRs: causal and temporal relation scheme for semantic annotation of event structures. In: Proceedings of the 4th Workshop on Events: Definition, Detection, Coreference, and Representation, pp. 51–61 (2016)
Allen, J.F.: Towards a general theory of action and time. Artif. Intell. 23(2), 123–154 (1984)
López, V., Fernández, A., García, S., Palade, V., Herrera, F.: An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf. Sci. 250, 113–141 (2013)
He, H., Ma, Y.: Imbalanced Learning: Foundations, Algorithms, and Applications. Wiley, Hoboken (2013)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Acknowledgments
This research is supported by Telekom Malaysia under the TM R&D Grant Scheme (No: MMUE/150061).
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Kok, L., Chua, SL., Ho, CK., Foo, L.K., Ramly, M.R.B.M. (2017). Annotating Network Service Fault Based on Temporal Interval Relations. In: Wang, G., Atiquzzaman, M., Yan, Z., Choo, KK. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2017. Lecture Notes in Computer Science(), vol 10658. Springer, Cham. https://doi.org/10.1007/978-3-319-72395-2_9
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DOI: https://doi.org/10.1007/978-3-319-72395-2_9
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