Abstract
Complex event logs in industrial applications can often be represented as graphs in order to conveniently model their multi-relational complex characteristics. Then, appropriate methods for analysis and mining are required, in order to provide insights that cover the relevant analytical questions and are understandable to humans. This paper presents a framework for such computational sensemaking on industrial event logs utilizing graph summarization techniques. We demonstrate the efficacy of the proposed approach on a real-world industrial dataset.
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Liu, Y., Safavi, T., Dighe, A., Koutra, D.: Graph summarization methods and applications: a survey. ACM Comput. Surv. (CSUR) 51(3), 62 (2018)
Atzmueller, M.: Declarative aspects in explicative data mining for computational sensemaking. In: Seipel, D., Hanus, M., Abreu, S. (eds.) WFLP/WLP/INAP 2017. LNCS (LNAI), vol. 10997, pp. 97–114. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00801-7_7
Van Der Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes, vol. 2. Springer, Heidelberg (2011)
Munoz-Gama, J., Carmona, J., van der Aalst, W.M.P.: Single-entry single-exit decomposed conformance checking. Inf. Syst. 46, 102–122 (2014)
Vaarandi, R.: A data clustering algorithm for mining patterns from event Lyuogs. In: Proceedings of the IEEE Workshop on IP Operations & Management, pp. 119–126. IEEE (2003)
Burns, L., Hellerstein, J., Ma, S., Perng, C., Rabenhorst, D., Taylor, D.: A systematic approach to discovering correlation rules for event management. In: Proceedings of the IFIP/IEEE IM, pp. 345–359 (2001)
Deza, M.M., Deza, E.: Encyclopedia of Distances, pp. 1–583. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-662-44342-2
McInnes, L., Healy, J., Astels, S.: HDBSCAN: hierarchical density based clustering. J. Open Source Softw. 2(11) (2017)
Riondato, M., García-Soriano, D., Bonchi, F.: Graph summarization with quality guarantees. Data Min. Knowl. Discov. 31(2), 314–349 (2017)
LeFevre, K., Terzi, E.: Grass: graph structure summarization. In: Proceedings of SDM, pp. 454–465 (2010)
Shen, Z., Ma, K.L., Eliassi-Rad, T.: Visual analysis of large heterogeneous social networks by semantic and structural abstraction. IEEE TVCG 12(6), 1427–1439 (2006)
Li, T., et al.: Flap: an end-to-end event log analysis platform for system management. In: Proceedings of SIGKDD, pp. 1547–1556. ACM (2017)
Zaki, M.J.: SPADE: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1–2), 31–60 (2001)
Pons, P., Latapy, M.: Computing communities in large networks using random walks. In: Yolum, I., Güngör, T., Gürgen, F., Özturan, C. (eds.) ISCIS 2005. LNCS, vol. 3733, pp. 284–293. Springer, Heidelberg (2005). https://doi.org/10.1007/11569596_31
Atzmueller, M., et al.: Big data analytics for proactive industrial decision support. atp edition 58(9) (2016)
Wilcke, X., Bloem, P., de Boer, V.: The knowledge graph as the default data model for learning on heterogeneous knowledge. Data Sci. 1, 1–19 (2017)
Sternberg, E., Atzmueller, M.: Knowledge-based mining of exceptional patterns in logistics data: approaches and experiences in an Industry 4.0 context. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G.A., Raś, Z.W. (eds.) ISMIS 2018. LNCS (LNAI), vol. 11177, pp. 67–77. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01851-1_7
Atzmueller, M., Güven, C., Seipel, D.: Towards Generating Explanations for ASP-Based Link Analysis using Declarative Program Transformations, University of Cottbus, Germany
Wick, M.R., Thompson, W.B.: Reconstructive expert system explanation. Artif. Intell. 54(1–2), 33–70 (1992)
Roth-Berghofer, T.R., Richter, M.M.: On explanation. Künstl. Intell. 22(2), 5–7 (2008)
Atzmueller, M., Roth-Berghofer, T.: The mining and analysis continuum of explaining uncovered. In: Proceedings of SGAI International Conference on Artificial Intelligence (AI 2010), Cambridge, UK, pp. 273–278 (2010)
Biran, O., Cotton, C.: Explanation and justification in machine learning: a survey. In: IJCAI 2017 Workshop on Explainable AI, pp. 8–13 (2017)
Atzmueller, M.: Onto explicative data mining: exploratory, interpretable and explainable analysis. In: Proceedings of Dutch-Belgian Database Day, TU Eindhoven, Netherlands (2017)
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This work has been supported by Interreg NWE, project Di-Plast - Digital Circular Economy for the Plastics Industry.
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Bloemheuvel, S., Kloepper, B., Atzmueller, M. (2019). Graph Summarization for Computational Sensemaking on Complex Industrial Event Logs. In: Di Francescomarino, C., Dijkman, R., Zdun, U. (eds) Business Process Management Workshops. BPM 2019. Lecture Notes in Business Information Processing, vol 362. Springer, Cham. https://doi.org/10.1007/978-3-030-37453-2_34
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DOI: https://doi.org/10.1007/978-3-030-37453-2_34
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