Abstract
Business processes are dynamic and change due to diverse factors. While existing approaches aim to detect drifts in the process structure, TESSERACT looks for temporal drifts in activity interim times. This orthogonal view on the process extends the traditional data cube of events - case id, activities and timestamps - by a fourth dimension and improves the operational support by a visualization of temporal drifts in real-time.
Insights about temporal deviations lead to an augmented awareness of imminent failures or improved service times. The detection of related structural concept drifts can be improved by early warning, as operation times of critical parts often increase before they catastrophically fail.
Similar content being viewed by others
References
Aminikhanghahi, S., Cook, D.J.: A survey of methods for time series change point detection. Knowl. Inf. Syst. 51(2), 339–367 (2017)
Backus, P., Janakiram, M., Mowzoon, S., Runger, C., Bhargava, A.: Factory cycle-time prediction with a data-mining approach. IEEE Trans. Semicond. Manuf. 19(2), 252–258 (2006)
Bolt, A., Sepúlveda, M.: Process remaining time prediction using query catalogs. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013. LNBIP, vol. 171, pp. 54–65. Springer, Cham (2014). doi:10.1007/978-3-319-06257-0_5
Bose, R.P., van der Aalst, W.M.P., Zliobaite, I., Pechenizkiy, M.: Dealing with concept drifts in process mining. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 154–171 (2014)
Bose, R.P.J.C., Aalst, W.M.P., Žliobaitė, I., Pechenizkiy, M.: Handling concept drift in process mining. In: Mouratidis, H., Rolland, C. (eds.) CAiSE 2011. LNCS, vol. 6741, pp. 391–405. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21640-4_30
Burattin, A., Sperduti, A., van der Aalst, W.M.P.: Heuristics miners for streaming event data (2012). arXiv:1212.6383
Burattin, A., Sperduti, A., van der Aalst, W.M.P.: Control-flow discovery from event streams. In: Congress on Evolutionary Computation (IEEE WCCI CEC) (2014)
Cabanillas, C., Di Ciccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: International Conference on Business Process Management, pp. 424–432. Springer (2014)
Carmona, J., Gavaldà, R.: Online techniques for dealing with concept drift in process mining. In: Hollmén, J., Klawonn, F., Tucker, A. (eds.) IDA 2012. LNCS, vol. 7619, pp. 90–102. Springer, Heidelberg (2012). doi:10.1007/978-3-642-34156-4_10
de Leoni, M., Mannhardt, F.: Road traffic fine management process (2015)
Fan, B., Andersen, D.G., Kaminsky, M., Mitzenmacher, M.D.: Cuckoo filter: Practically better than bloom. In: Proceedings of the 10th ACM International on Conference on emerging Networking Experiments and Technologies, pp. 75–88. ACM (2014)
Gal, A., Mandelbaum, A., Schnitzler, F., Senderovich, A., Weidlich, M.: On predicting traveling times in scheduled transportation. In: Proceedings of the 2nd International Conference on Mining Urban Data, Vol. 1392, pp. 88–89. CEUR-WS. org (2015)
Hassani, M., Siccha, S., Richter, F., Seidl, T.: Efficient process discovery from event streams using sequential pattern mining. In: IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 1366–1373 (2015)
Maaradji, A., Dumas, M., Rosa, M., Ostovar, A.: Fast and accurate business process drift detection. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 406–422. Springer, Cham (2015). doi:10.1007/978-3-319-23063-4_27
Kuma, M.V.M., Thomas, L., Annappa, B: Capturing the sudden concept drift in process mining. In: BPM Workshops, pp. 132–143 (2015)
Polato, M., Sperduti, A., Burattin, A., de Leoni, M.: Data-aware remaining time prediction of business process instances. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 816–823. IEEE (2014)
Schlimmer, J.C., Granger, R.H.: Beyond incremental processing: Tracking concept drift. In: National Conference AI, pp. 502–507 (1986)
Schubert, E., Weiler, M., Kriegel, H.-P.: Signitrend: scalable detection of emerging topics in textual streams by hashed significance thresholds. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 871–880. ACM (2014)
Senderovich, A., Weidlich, M., Gal, A., Mandelbaum, A.: Queue mining – Predicting delays in service processes. In: Jarke, M., Mylopoulos, J., Quix, C., Rolland, C., Manolopoulos, Y., Mouratidis, H., Horkoff, J. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 42–57. Springer, Cham (2014). doi:10.1007/978-3-319-07881-6_4
van der Aalst, W.: Process Mining: Data science in action. Springer, Heidelberg (2016)
Van der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Inf. Syst. 36(2), 450–475 (2011)
van Dongen, B.F.: Bpi challenge 2017 - offer log (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Richter, F., Seidl, T. (2017). TESSERACT: Time-Drifts in Event Streams Using Series of Evolving Rolling Averages of Completion Times. In: Carmona, J., Engels, G., Kumar, A. (eds) Business Process Management. BPM 2017. Lecture Notes in Computer Science(), vol 10445. Springer, Cham. https://doi.org/10.1007/978-3-319-65000-5_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-65000-5_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-64999-3
Online ISBN: 978-3-319-65000-5
eBook Packages: Computer ScienceComputer Science (R0)