Abstract
Complex systems, such as communication networks, generate thousands of new data points about the system state every minute. Even if faults are rare events, they can easily propagate, which makes it challenging to distinguish root causes of errors from effects among the thousands of highly correlated alerts appearing simultaneously in high volumes of data. In this context, the need for automated Root Cause Analysis (RCA) tools emerges, along with the creation of a causal model of the real system, which can be regarded as a digital twin. The advantage of such model is twofold: (i) it assists in reasoning on the system state, given partial system observations; and (ii) it allows generating labelled synthetic data, in order to benchmark causal discovery techniques or create previously unseen faulty scenarios (counterfactual reasoning). The problem addressed in this paper is the creation of a causal model which can mimic the behavior of the real system by encoding the appearance, propagation and persistence of faults through time. The model extends Structural Causal Models (SCMs) with the use of logical noisy-OR gates to incorporate the time dimension and represent propagation behaviors. Finally, the soundness of the approach is experimentally verified by generating synthetic alert logs and discovering both the structure and parameters of the underlying causal model.
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References
van der Aalst, W.M., Hinz, O., Weinhardt, C.: Resilient digital twins (2021)
Agnieszka, O., Marek, J.D., Hanna, W.: Learning Bayesian network parameters from small data sets: application of noisy-or gates. Int. J. Approx. Reason. 27(2), 165–182 (2001)
Aldrich, J.: Autonomy. Oxford Econ. Pap. 41(1), 15–34 (1989)
Andrews, B., Ramsey, J., Cooper, G.F.: Learning high-dimensional directed acyclic graphs with mixed data-types. PMLR 104, 4–21 (2019)
Banerjee, A., Dalal, R., Mittal, S., Joshi, K.P.: Generating digital twin models using knowledge graphs for industrial production lines. UMBC Information Systems Department (2017)
Bollen, K.A.: Structural equation models with observed variables. Struct. Eqn. Latent Variables 80–150 (1989)
Boschert, S., Heinrich, C., Rosen, R.: Next generation digital twin. In: Proceedings of tmce, vol. 2018, pp. 7–11. Las Palmas de Gran Canaria, Spain (2018)
Fallet-Fidry, G., Weber, P., Simon, C., Iung, B., Duval, C.: Evidential network-based extension of leaky noisy-or structure for supporting risks analyses. IFAC Proc. Vol. 45(20), 672–677 (2012)
Fenton, N.E., Noguchi, T., Neil, M.: An extension to the noisy-or function to resolve the “explaining away’’ deficiency for practical Bayesian network problems’, ieee trans. Knowl. Data Eng. 31, 2441–2445 (2019)
Hoover, K.D.: Causality in economics and econometrics. In: The New Palgrave Dictionary of Economics, pp. 1–13. Palgrave Macmillan, UK (2017)
Lawrence, A.R., Kaiser, M., Sampaio, R., Sipos, M.: Data generating process to evaluate causal discovery techniques for time series data. In: Causalens NIPS 2020 workshop (2020)
Li, C., Mahadevan, S., Ling, Y., Wang, L., Choze, S.: A dynamic Bayesian network approach for digital twin. In: 19th AIAA Non-Deterministic Approaches Conference, p. 1566 (2017)
Liang, R., Liu, F., Liu, J.: A belief network reasoning framework for fault localization in communication networks. Sensors 20, 6950 (2020)
Mirylenka, K., Cormode, G., Palpanas, T., Srivastava, D.: Conditional heavy hitters: detecting interesting correlations in data streams. VLDB J. 24(3), 395–414 (2015). https://doi.org/10.1007/s00778-015-0382-5
Nauta, M., Bucur, D., Seifert, C.: Causal discovery with attention-based convolutional neural networks. Mach. Learn. Knowl. Extract. 1(1), 312–340 (2019). https://www.mdpi.com/2504-4990/1/1/19
Oniśko, A., Druzdzel, M.J., Wasyluk, H.: Learning Bayesian network parameters from small data sets: application of noisy-or gates. Int. J. Approx. Reason. 27(2), 165–182 (2001)
Pamfil, R., et al.: DYNOTEARS: structure learning from time-series data. In: International Conference on Artificial Intelligence and Statistics (2020)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, Inc., Burlington (1988)
Pearl, J.: Causality. Cambridge University Press, Cambridge (2009)
Pearl, J., Glymour, M., Jewell, N.P.: Causal Inference in Statistics: A Primer. Wiley, Hoboken (2016)
Ramsey, J., Malinsky, D., Bui, K.V.: algcomparison: comparing the performance of graphical structure learning algorithms with TETRAD. arXiv preprint arXiv:1607.08110 (2016)
Runge, J.: Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. In: Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI). PMLR (2020)
Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., Sejdinovic, D.: PCMCI - detecting causal associations in large nonlinear time series datasets. Sci. Adv 5, 11 (2019)
Thulasiraman, K., Swamy, M.N.S.: Graphs: Theory and Algorithms. Wiley, Hoboken (2011)
Zhou, K., Martin, A., Pan, Q.: The belief noisy-or model applied to network reliability analysis. ArXiv abs/1606.01116 (2016)
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Jakovljevic, L., Kostadinov, D., Aghasaryan, A., Palpanas, T. (2022). Towards Building a Digital Twin of Complex System Using Causal Modelling. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1072. Springer, Cham. https://doi.org/10.1007/978-3-030-93409-5_40
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