Abstract
Business processes of some domains are highly dynamic and increasingly complex due to their dependencies on a multitude of services provided by various providers. The quality of services directly impacts the business process’s efficiency. A first prerequisite for any optimization initiative requires a better understanding of the deployed business processes. However, the business processes are either not documented at all or are only poorly documented. Since the actual behaviour of the business processes and underlying services can change over time it is required to detect the dynamically changing behaviour in order to carry out correct analyses. This paper presents and evaluates the integration of the Dynamic Construct Competition Miner (DCCM) as process monitor in the TIMBUS architecture. The DCCM discovers business processes and recognizes changes directly from an event stream at run-time. The evaluation is carried out in the context of an industrial use-case from the eHealth domain. We will describe the key aspects of the use-case and the DCCM as well as present the relevant evaluation results.
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Notes
- 1.
DrugFusion downloads adverse events report published by United States Food and Drug Administration (http://www.fda.gov) every quarter.
- 2.
For sequence [EDE] both relations are true: E appears before D and D appears before E.
- 3.
Both relations “appears before first” and “appears before” are always true for activities within the loop, e.g. for both sequences [EDE] (normal loop) and [EDED] (loop over sequence) E “appears before” D and vice versa.
References
Antunes, G., Caetano, A., Bakhshandeh, M., Mayer, R., Borbinha, J.: Using ontologies to integrate multiple enterprise architecture domains. In: Abramowicz, W. (ed.) BIS Workshops 2013. LNBIP, vol. 160, pp. 61–72. Springer, Heidelberg (2013)
Butt, T., Cox, A., Oyebode, J., Ferner, R.: Internet accounts of serious adverse drug reactions a study of experiences of Stevens-Johnson syndrome and toxic epidermal necrolysis. Drug Saf. 35(12), 1159–1170 (2012)
Galushka, M., Gilani, W.: DrugFusion - retrieval knowledge management for prediction of adverse drug events. In: Abramowicz, W., Kokkinaki, A. (eds.) BIS 2014. LNBIP, vol. 176, pp. 13–24. Springer, Heidelberg (2014)
Galushka, M., Taylor, P., Gilani, W., Thomson, J., Strodl, S., Neumann, M.: Digital preservation of business processes with TIMBUS architecture. In: Proceedings of 9th International Conference on Preservation of Digital Objects IPRES2012, pp. 117–125 (2012)
Gilani, W., Redlich, D., Galushka, M., Molka, T., Du, Y.: TIMBUS: Digital preservation for timeless business processes and services. In: 23rd Proceedings of eChallenges Conference (e-2013) (2013)
Huang, Y., Lin, S., Chiu, C., Yeh, H., Soo, V.: Probability analysis on associations of adverse drug events with drug-drug interactions. In: BIBE 2007, pp. 1308–1312 (2007)
Jin, H., Chen, J., He, H., Kelman, C., McAullay, D., O’Keefe, C.: signaling potential adverse drug reactions from administrative health databases. IEEE Trans. Knowl. Data Eng. 22(6), 839–853 (2010)
Jin, H., Chen, J., He, H., Williams, G., Kelman, C., O’Keefe, C.: Mining unexpected temporal associations: applications in detecting adverse drug reactions. IEEE Trans. Inf. Technol. Biomed. 12(4), 488–500 (2008)
Ji, Y., Ying, H., Dews, P., Mansour, A., Tran, J., Miller, R., Massanari, R.M.: A potential causal association mining algorithm for screening adverse drug reactions in postmarketing surveillance. IEEE Trans. Inf. Technol. Biomed. 15(3), 428–437 (2011)
Ko, R.K.L.: A computer scientist’s introductory guide to business process management (BPM). Crossroads J., ACM 15(4), 4 (2009)
Koutkias, V., Kilintzis, V., Stalidis, G., Lazou, K., Nis, J., Durand-Texte, L., McNair, P., Beuscart, R., Maglaveras, N.: Knowledge engineering for adverse drug event prevention: on the design and development of a uniform, contextualized and sustainable knowledge-based framework. J. Biomed. Inf. 45(3), 495–506 (2012)
Krska, J., Cox, A.: Adverse drug reactions. Clin. Pharmacol. Ther. 91, 467–474 (2012)
Luckham, D.: The Power of Events: An Introduction to Complex Event Processing. Addison-Wesley Professional, Reading (2002)
Molka, T., Redlich, D., Drobek, M., Zeng, X.-J., Gilani, W.: Diversity guided evolutionary mining of hierarchical process models. In: Genetic and Evolutionary Computation Conference (GECCO 2015), ACM (2015) http://dx.doi.org/10.1145/2739480.2754765
Rao, S., Gupta, R.: Implementing improved algorithm over APRIORI data mining association rule algorithm. Int. J Comput. Sci. Technol. 1, 489–493 (2012)
Redlich, D., Molka, T., Gilani, W., Blair, G., Rashid, A.: Constructs competition miner: process control-flow discovery of BP-domain constructs. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 134–150. Springer, Heidelberg (2014)
Redlich, D., Molka, T., Blair, G., Rashid, A., Gilani, W.: Scalable dynamic business process discovery with the constructs competition miner. In: Proceedings of the 4th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2014), CEUR 1293, pp. 91–107 (2014)
Van Der Aalst, W., Weijters, A., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)
Van Der Aalst, W.: Process Mining - Discovery Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)
Weijters, A., Van Der Aalst, W., de Medeiros, A.A.: Process Mining with the Heuristics Miner-algorithm. BETA Working Paper Series, WP 166, Eindhoven University of Technology (2006)
Acknowledgments
Project partially funded by the European Commission under the 7th Framework Programme for research and technological development and demonstration activities under grant agreement 269940, TIMBUS project (http://timbusproject.net/).
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Redlich, D., Galushka, M., Molka, T., Gilani, W., Blair, G., Rashid, A. (2015). Evaluation of the Dynamic Construct Competition Miner for an eHealth System. In: Abramowicz, W. (eds) Business Information Systems. BIS 2015. Lecture Notes in Business Information Processing, vol 208. Springer, Cham. https://doi.org/10.1007/978-3-319-19027-3_10
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DOI: https://doi.org/10.1007/978-3-319-19027-3_10
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