Abstract
This work deals with redesigning business process models, e.g., in BPMN, based on cost-based optimization techniques that were initially proposed for data analytics workflows. More specifically, it discusses execution cost and cycle time improvements through treating business processes in the same way as data-centric workflows. The presented solutions are cost-based, i.e., they employ quantitative metadata and cost models. The advantage of this approach is that business processes can benefit from recent advances in data-intensive workflow optimization similarly to the manner they nowadays benefit from additional data analytics areas, e.g., in the area of process mining. Concrete use cases are presented that are capable of demonstrating that even in small, more conservative cases, the benefits are significant. The contribution of this work is to show how to automatically optimize the model structure of a given process in terms of the ordering of tasks and how to perform resource allocation under contradicting objectives. Finally, the work identifies open issues in developing end-to-end business process redesign solutions with regards to the case studies considered.
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Notes
- 1.
Note that these constraints refer to the process model structure; additional execution constraints, e.g., two tasks share the same resource and thus cannot run simultaneously, affect the cost models that quantify the optimization objectives (see also Sect. 4.2).
- 2.
taken from https://www.businessprocessincubator.com/.
References
Appian: Low-code platform and bpm software for digital transformation. https://www.appian.com/
BIMP - the business process simulator. http://bimp.cs.ut.ee/
Bizagi - digital transformation and business process management bpm. https://www.bizagi.com/en
Camunda bpm: Workflow and decision automation platform. https://camunda.com/
van der Aalst, W.M.P.: Re-engineering knock-out processes. Decis. Support Syst. 30(4), 451–468 (2001)
van der Aalst, W.M.P.: Process Mining - Discovery, Conformance and Enhancementof Business Processes. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19345-3
van der Aalst, W.M.P.: Spreadsheets for business process management: using process mining to deal with “events” rather than “numbers”? Bus. Proc. Manag. J. 24(1), 105–127 (2018)
Agrawal, K., Benoit, A., Dufossé, F., Robert, Y.: Mapping filtering streaming applications. Algorithmica 62(1–2), 258–308 (2012)
Augusto, A., Conforti, R., Dumas, M., Rosa, M.L.: Split miner: discovering accurate and simple business process models from event logs. In: 2017 IEEE International Conference on Data Mining, ICDM 2017, New Orleans, LA, USA, 18–21 November 2017, pp. 1–10 (2017)
Augusto, A., Conforti, R., Dumas, M., Rosa, M.L., Bruno, G.: Automated discovery of structured process models from event logs: the discover-and-structure approach. Data Knowl. Eng. 117, 373–392 (2018)
Jagadeesh Chandra Bose, R.P., van der Aalst, W.: Trace alignment in process mining: opportunities for process diagnostics. In: Hull, R., Mendling, J., Tai, S. (eds.) BPM 2010. LNCS, vol. 6336, pp. 227–242. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15618-2_17
Brownlee, J.: Clever algorithms: nature-inspired programming recipes (2011)
Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: Discovering and navigating a collection of process models using multiple quality dimensions. In: Business Process Management Workshops - BPM 2013 International Workshops, Beijing, China, 26 August 2013, Revised Papers, pp. 3–14 (2013)
Buijs, J.C.A.M., van Dongen, B.F., van der Aalst, W.M.P.: Mining configurable process models from collections of event logs. In: Business Process Management - 11th International Conference, BPM 2013, Beijing, China, 26–30 August 2013, Proceedings, pp. 33–48 (2013)
Buzacott, J.A.: Commonalities in reengineered business processes: models and issues. Manag. Sci. 42(5), 768–782 (1996)
Deshpande, A., Hellerstein, L.: Parallel pipelined filter ordering with precedence constraints. ACM Trans. Algorithms 8(4), 41:1–41:38 (2012)
Dumas, M., Rosa, M.L., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management, 2nd edn. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-642-33143-5
Falk, T., Griesberger, P., Leist, S.: Patterns as an artifact for business process improvement - insights from a case study. In: vom Brocke, J., Hekkala, R., Ram, S., Rossi, M. (eds.) DESRIST 2013. LNCS, vol. 7939, pp. 88–104. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38827-9_7
Gounaris, A.: Towards automated performance optimization of BPMN business processes. In: New Trends in Databases and Information Systems - ADBIS 2016 Short Papers and Workshops, pp. 19–28 (2016)
Gounaris, A., Kougka, G., Tous, R., Montes, C.T., Torres, J.: Dynamic configuration of partitioning in spark applications. IEEE Trans. Parallel Distrib. Syst. 28(7), 1891–1904 (2017)
Ibaraki, T., Kameda, T.: On the optimal nesting order for computing N-relational joins. ACM Trans. Database Syst. 9(3), 482–502 (1984)
Indulska, M., zur Muehlen, M., Recker, J.: Measuring method complexity: the case of the business process modeling notation. Technical report, BPM Center Report BPM-09-03 (2009). BPMcenter.org
Jennings, N.R., Norman, T.J., Faratin, P., O’Brien, P., Odgers, B.: Autonomous agents for business process management. Appl. Artif. Intell. 14(2), 145–189 (2000)
Kiepuszewski, B., ter Hofstede, A.H.M., Bussler, C.J.: On structured workflow modelling. In: Wangler, B., Bergman, L. (eds.) CAiSE 2000. LNCS, vol. 1789, pp. 431–445. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45140-4_29
Köpke, J., Franceschetti, M., Eder, J.: Optimizing data-flow implementations for inter-organizational processes. Distrib. Parallel Databases 37, 651–695 (2018)
Kougka, G., Gounaris, A.: Cost optimization of data flows based on task re-ordering. In: Hameurlain, A., Küng, J., Wagner, R., Akbarinia, R., Pacitti, E. (eds.) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIII. LNCS, vol. 10430, pp. 113–145. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-662-55696-2_4
Kougka, G., Gounaris, A.: Optimal task ordering in chain data flows: exploring the practicality of non-scalable solutions. In: Bellatreche, L., Chakravarthy, S. (eds.) DaWaK 2017. LNCS, vol. 10440, pp. 19–32. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64283-3_2
Kougka, G., Gounaris, A.: Optimization of data flow execution in a parallel environment. Distrib. Parallel Databases (2018). https://doi.org/10.1007/s10619-018-7243-3
Kougka, G., Gounaris, A., Simitsis, A.: The many faces of data-centric workflow optimization: a survey. Int. J. Data Sci. Anal. 6(2), 81–107 (2018)
Kougka, G., Gounaris, A., Tsichlas, K.: Practical algorithms for execution engine selection in data flows. Future Generation Comp. Syst. 45, 133–148 (2015)
Krishnamurthy, R., Boral, H., Zaniolo, C.: Optimization of nonrecursive queries. In: VLDB, pp. 128–137 (1986)
La Rosa, M., Dumas, M., ter Hofstede, A.H.M., Mendling, J.: Configurable multi-perspective business process models. Inf. Syst. 36(2), 313–340 (2011)
Maggi, F.M., Mooij, A.J., van der Aalst, W.M.P.: User-guided discovery of declarative process models. In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2011, part of the IEEE Symposium Series on Computational Intelligence 2011, Paris, France, 11–15 April 2011, pp. 192–199 (2011)
Mendling, J.: Metrics for Process Models: Empirical Foundations of Verification, Error Prediction, and Guidelines for Correctness. Lecture Notes in Business Information Processing, vol. 6. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89224-3
Michailidou, A., Gounaris, A.: Bi-objective traffic optimization in geo-distributed data flows. Big Data Res. 16, 36–48 (2019)
Nardelli, M., Cardellini, V., Grassi, V., Presti, F.L.: Efficient operator placement for distributed data stream processing applications. IEEE Trans. Parallel Distrib. Syst. 30(8), 1753–1767 (2019)
Pesic, M., van der Aalst, W.M.P.: A declarative approach for flexible business processes management. In: Eder, J., Dustdar, S. (eds.) BPM 2006. LNCS, vol. 4103, pp. 169–180. Springer, Heidelberg (2006). https://doi.org/10.1007/11837862_18
Pesic, M., Schonenberg, H., van der Aalst, W.M.P.: DECLARE: full support for loosely-structured processes. In: 11th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2007), pp. 287–300 (2007)
Polyvyanyy, A., García-Bañuelos, L., Dumas, M.: Structuring acyclic process models. Inf. Syst. 37(6), 518–538 (2012)
Polyvyanyy, A., Ouyang, C., Barros, A., van der Aalst, W.M.P.: Process querying: enabling business intelligence through query-based process analytics. Decis. Support Syst. 100, 41–56 (2017)
Pourmasoumi, A., Bagheri, E.: Business process mining. CoRR abs/1607.00607 (2016)
Rheinländer, A., Leser, U., Graefe, G.: Optimization of complex dataflows with user-defined functions. ACM Comput. Surv. 50(3), 38:1–38:39 (2017)
Rosa, M.L., et al.: Managing process model complexity via abstract syntax modifications. IEEE Trans. Ind. Inf. 7(4), 614–629 (2011)
Sakr, S., Maamar, Z., Awad, A., Benatallah, B., van der Aalst, W.M.P.: Business process analytics and big data systems: a roadmap to bridge the gap. IEEE Access 6, 77308–77320 (2018)
Schunselaar, D.: Configurable process trees: elicitation, analysis, and enactment (2016)
Simitsis, A., Wilkinson, K., Castellanos, M., Dayal, U.: Optimizing analytic data flows for multiple execution engines. In: SIGMOD Conference, pp. 829–840 (2012)
Simitsis, A., Wilkinson, K., Dayal, U., Castellanos, M.: Optimizing ETL workflows for fault-tolerance. In: ICDE, pp. 385–396 (2010)
Tao, J., Deokar, A.V.: An organizational mining approach based on behavioral process patterns. In: 20th Americas Conference on Information Systems, AMCIS 2014, Savannah, Georgia, USA, 7–9 August 2014 (2014)
Tsakalidis, G., Vergidis, K., Kougka, G., Gounaris, A.: Eligibility of BPMN models for business process redesign. Information 10(7), 225 (2019)
Vanhatalo, J., Völzer, H., Koehler, J.: The refined process structure tree. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 100–115. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85758-7_10
Varol, Y.L., Rotem, D.: An algorithm to generate all topological sorting arrangements. Comput. J. 24(1), 83–84 (1981)
Vergidis, K., Tiwari, A., Majeed, B.: Business process analysis and optimization: beyond reengineering. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 38(1), 69–82 (2008)
Wolf, F., Brendle, M., May, N., Willems, P.R., Sattler, K., Grossniklaus, M.: Robustness metrics for relational query execution plans. PVLDB 11(11), 1360–1372 (2018)
Yilmaz, O., Karagoz, P.: Generating performance improvement suggestions by using cross-organizational process mining. In: Proceedings of the 5th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2015), Vienna, Austria, 9–11 December 2015, pp. 3–17 (2015)
Zaharia, M., et al.: Apache spark: a unified engine for big data processing. Commun. ACM 59(11), 56–65 (2016)
van Zelst, S.J., van Dongen, B.F., van der Aalst, W.M.P.: Event stream-based process discovery using abstract representations. Knowl. Inf. Syst. 54(2), 407–435 (2018)
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Kougka, G., Varvoutas, K., Gounaris, A., Tsakalidis, G., Vergidis, K. (2020). On Knowledge Transfer from Cost-Based Optimization of Data-Centric Workflows to Business Process Redesign. In: Hameurlain, A., Tjoa, A. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIII. Lecture Notes in Computer Science(), vol 12130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-62199-8_3
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