Abstract
Process Mining (PM) approaches for the analysis of time series bring interesting new perspectives because of the structural and behavioral information that could be extracted when the series are represented as event logs. This paper presents a Process Mining-based, supervised machine learning approach for constructing time series classifiers using graph kernels. A collection of time series of different kinds (normal, cyclic, with upward/downward trends and with upward/downward level shifts) is pre-processed by converting the real-valued series into event logs. Directly Follows Graphs (DFG) for activities and resources were discovered using the Inductive Miner algorithm and eight graph kernels were used for evaluating the similarities between the DFGs. These kernels were used in conjunction with class membership information for obtaining classification models in the form of support vector machines. High-performance classification models were found, particularly when using additively aggregated kernels coming from the combination of those obtained separately from activities and resources. The PM-based models have cross-validation mean accuracies comparable with those based on dynamic time warping, as well as top precision and recall metrics for most of the graph kernels. The results obtained are preliminary, but illustrate the potential of PM methods for time series analysis.
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References
Aiolli, F., Da San Martino, G., Sperduti, A., Moschitti, A.: Fast on-line kernel learning for trees. In: Proceedings of the 6th International Conference on Data Mining, pp. 787–791 (2006)
Alcock, R.J., Manolopoulos, Y.: Time-series similarity queries employing a feature-based approach. In: Proceedings 7th Hellenic Conference on Informatics, Ioannina, Greece, August 27–29, 1999
Berti, A., van Zelst, S.J., van der Aalst, W.M.P.: Process mining for python (PM4Py): bridging the gap between process-and data science. In: Proceedings of ICPM Demo Track 2019, co-located with 1st International Conference on Process Mining (ICPM 2019), Aachen, Germany, June 24–26, 2019, pp. 13–16 (2019)
Borgwardt, M., Kriegel, H.P.: Shortest-path kernels on graphs. In: Proceedings of the 5th International Conference on Data Mining, pp. 74–81 (2005)
Breitling, R., Armengaud, P., Amtmann, A., Herzyk, P.: Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 573, 83–92 (2004)
Costa, F., De Grave, K.: Fast neighborhood subgraph pairwise distance kernel. In: Proceedings of the 26th International Conference on Machine Learning, pp. 255–262 (2010)
Dua, D., Graff, C.: UCI machine learning repository (2017)
Herbrich, R.: Learning Kernel Classifiers: Theory and Algorithms. MIT Press, Cambridge (2002)
Hido, S., Kashima, H.: A linear-time graph kernel. In: Proceedings of the 9th International Conference on Data Mining, pp. 179–188 (2009)
Kataoka, T., Inokuchi, A.: Hadamard code graph kernels for classifying graphs. In: Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods, pp. 24–32 (2016)
Kriege, N., Mutzel, P.: Subgraph matching kernels for attributed graphs. In: Proceedings of the 29th International Conference on Machine Learning, pp. 291–298 (2012)
Da San Martino, G., Navarin, N., Sperduti, A.: A tree-based kernel for graphs. In: Proceedings of the 2012 SIAM International Conference on Data Mining (2012)
Nikolentzos, G., Meladianos, P., Vazirgiannis, M.: Matching node embeddings for graph similarity. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 2429–2435 (2017)
Pham, D.T., Chan, A.B.: Control chart pattern recognition using a new type of self organizing neural network. Proc. Inst. Mech. Eng. 212(1), 115–127 (1998)
Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Shen, C., Vogelstein, J.T.: The exact equivalence of distance and kernel methods in hypothesis testing. ArXiv 1806.05514v4 (2019). Accessed July 2020
Siglidis, G., Nikolentzos, G., Limnios, S., Giatsidis, C., Skianis, K., Vazirgiannis, M.: Grakel: a graph kernel library in python. J. Mach. Learn. Res. 21(54), 1–5 (2020)
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311–329. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38697-8_17
Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Scalable process discovery with guarantees. In: Gaaloul, K., Schmidt, R., Nurcan, S., Guerreiro, S., Ma, Q. (eds.) CAISE 2015. LNBIP, vol. 214, pp. 85–101. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19237-6_6
Sugiyama, M., Borgwardt, K.M.: Halting in random walk kernels. In: Advances in Neural Information Processing Systems, pp. 1639–1647 (2015)
Tax, N., Sidorova, N., Haakma, R., van der Aalst, W.M.: Mining local process models with constraints efficiently: applications to the analysis of smart home data. In: Proceedings of IEEE 14th International Conference on Intelligent Environments (IE), pp. 56–63 (2018)
Process Mining Group TU/e. Event logs. what kind of data does process mining require? (2016). http://www.processmining.org/logs/start. Accessed May 2020
Valdés, J.J., Céspedes-González, Y., Tapping, K.: A process mining approach to the analysis of the structure of time series. In: FTC 2020 - Future Technologies Conference 2020 (accepted paper) (2020)
van der Alast, W., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011, Part I. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28108-2_19
Aalst, W.: Data science in action. In: Process Mining, pp. 3–23. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4_1
van der Aalst, W.M.P., Song, M.: Mining social networks: uncovering interaction patterns in business processes. In: Desel, J., Pernici, B., Weske, M. (eds.) BPM 2004. LNCS, vol. 3080, pp. 244–260. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-25970-1_16
Vishwanathan, S.V.N., Smola, A.J.: Fast kernels for string and tree matching. In: Proceedings of the 15th International Conference on Neural Information Processing Systems, pp. 585–592 (2002)
Weisfeiler, B., Lehman, A.A.: A reduction of a graph to a canonical form and an algebra arising during this reduction. Nauchno-Technicheskaya Informatsia 2(9), 12–16 (1968)
Reijers, H.A., van der Aalst, W.M., Song, M.: Discovering social networks from event logs. Comput. Support. Coop. Work (CSCW) 14(6), 549–593 (2005)
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Valdés, J.J., Céspedes-González, Y., Pou, A. (2022). Process Mining as a Time Series Analysis Tool via Graph Kernels. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2021, Volume 1. FTC 2021. Lecture Notes in Networks and Systems, vol 358. Springer, Cham. https://doi.org/10.1007/978-3-030-89906-6_53
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