Annak érdekében, hogy Önnek a legjobb élményt nyújtsuk "sütiket" használunk honlapunkon. Az oldal használatával Ön beleegyezik a "sütik" használatába.

Folyamatbányászat

2023. január 9., 11:00
A folyamatbányászathoz tartozó cikkek listája:

1.     van der Aalst, W. M. P. (2016). Process Mining: Data Science in Action. Springer, Heidelberg. doi: 10.1007/978-3-662-49851-4

2.     Task force on process mining : https://www.tf-pm.org/

3.     Wil van der Aalst, Joseph Calmona (eds.) : Process Mining Handbook, Lecture Notes in Business Information Processing, Springer (2022), doi: 10.1007/978-3-031-08848-3

4.     Emamjome, F., Andrews, R. and ter Hofstede, A. H. (2019). A case study lens on process mining in practice. In OTM Confederated International Conferences” On the Move to Meaningful Internet Systems” (pp. 127-145). Springer.

5.     van der Aalst, W.M.P.: Process Mining: Overview and Opportunities, ACM Transactions on Management Information Systems, 2012, vol. 3, no. 2, article 7.

6.     Agrawal, R., Gunopulos, D., Leymann, F.: Mining process models from workflow logs, in: Proceedings of the 6th International Conference on Extending Database Technology (EDBT'98), 1998, LNCS 1377, pp. 469-483.

7.     Cook, J., Wolf, A.: Discovering models of software processes from event-based data, ACM Transactions on Software Engineering and Methodology, 1998 (7), pp. 215–249.

8.     Datta, A.: Automating the discovery of AS-IS business process models: probabilistic and algorithmic approaches, Information Systems Research, 1998, vol. 9, pp. 275–301.

9.     Mannila, H., Meek, C.: Global partial orders from sequential data, in: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’00), 2000, pp. 161–168.

10.  van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workflow mining: discovering process models from event logs, IEEE Transactions on Knowledge and Data Engineering, 2004, vol. 16, pp. 1128–1142.

11.  Alves de Medeiros, A.K., van Dongen, B.F., van der Aalst, W.M.P. and Weijters, A.J.M.M.: Process Mining: Extending the Alpha-Algorithm to Mine Short Loops, BETA Working Paper Series, TU Eindho- ven, 2004, vol. 113.

12.  Wen, L., Wang, J., van der Aalst, W.M.P., Huang, B., Sun, J.: A novel approach for process mining based on event types, Journal of Intelligent Information Systems, 2009, vol. 32, pp. 163–190.

13.  Weijters, A.J.M.M., van der Aalst, W.M.P., Alves de Medeiros, A.K.: Process Mining with the Heuristics Miner algorithm, BETA Working Paper Series, 2006, TU Eindhoven, vol. 166.

14.  Günther, C.W., van der Aalst, W.M.P.: Fuzzy mining - adaptive process simplification based on multi-perspective metrics, in: Proceedings of the 5th International Conference on Business Process Management (BPM), 2007, LNCS 4714, pp. 328–343.

15.  Folino, F., Greco, G., Guzzo, A., Pontieri, L.: Discovering expressive process models from noised log data, in: Proceedings of the 2009 International Database Engineering & Applications Symposium, 2009, ACM, pp.162–172.

16.  Ferreira, H., Ferreira, D.: An integrated life cycle for workflow management based on learning and planning, International Journal of Cooperative Information Systems, 2006, vol. 15, pp. 485–505.

17.  Burattin, A., and Sperduti, A. (2010, September). PLG: a framework for the generation of business process models and their execution logs. In International Conference on Business Process Management (pp. 214-219). Springer, Berlin, Heidelberg.

18.  Liesaputra, V., Yongchareon, S., and Chaisiri, S.: (2016, Sept.) Efficient process model discovery using maximal pattern mining. In International Conference on Business Process Management, pp. 441-456), Springer, Cham.

19.  Alves de Medeiros, A.K., Weijters, A.J.M.M., van der Aalst, W.M.P.: Genetic process mining: an experimental evaluation, Data Mining and Knowledge Discovery, 2007, vol. 14, pp. 245–304.

20.  Oncina, J., García, P., and Vidal, E. (1993). Learning subsequential transducers for pattern recognition interpretation tasks, IEEE Transactions on Pattern Analysis and Machine Intelligence,15(5), 448-458.

21.  van der Aalst, W.M.P., Rubin, V., Verbeek, H.M.W., van Dongen, B.F., Kindler, Günther, C.W.: Process mining: a two-step approach to balance between underfitting and overfitting, Software and System Modeling, 2010 (9), pp. 87–111.

22.  Hoschele, M. and Zeller, A. (2017, May): Mining input grammars with AUTOGRAM. In 2017 IEEE/ACM 39th International Conference on Software Engineering Companion (pp. 31-34). IEEE.

23.  Antunes, C., & Oliveira, A. (2002, July). Using context-free grammars to constrain apriori-based algorithms for mining temporal association rules. In Proc. Workshop on Temporal Data Mining.

24.  Truong-Chi, T., Fournier-Viger, P. (2019). High-Utility Pattern Mining: Theory, Algorithms and Applications. In: A Survey of High Utility Sequential Pattern Mining, pp. 97-129. Springer, Cham

25.  Sommers, D., Menkovski, V.and Fahland, D. (2021). Process discovery using graph neural networks. In 2021 3rd International Conference on Process Mining (ICPM) (pp. 40-47). IEEE.

26.  Di Francescomarino, C. and Ghidini, C. (2022). Predictive process monitoring. Process Mining Handbook. LNBIP, 448, 320-346.

27.  Hingston, P. (2002). Using finite state automata for sequence mining. In ACSC (pp. 105-110).