Skip to main content
View expanded cover

Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIII pp 62–85Cite as

On Knowledge Transfer from Cost-Based Optimization of Data-Centric Workflows to Business Process Redesign

Part of the Lecture Notes in Computer Science book series (TLDKS,volume 12130)

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.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-662-62199-8_3
  • Chapter length: 24 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   59.99
Price excludes VAT (USA)
  • ISBN: 978-3-662-62199-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   74.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.

Notes

  1. 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. 2.

    taken from https://www.businessprocessincubator.com/.

References

  1. Appian: Low-code platform and bpm software for digital transformation. https://www.appian.com/

  2. BIMP - the business process simulator. http://bimp.cs.ut.ee/

  3. Bizagi - digital transformation and business process management bpm. https://www.bizagi.com/en

  4. Camunda bpm: Workflow and decision automation platform. https://camunda.com/

  5. van der Aalst, W.M.P.: Re-engineering knock-out processes. Decis. Support Syst. 30(4), 451–468 (2001)

    CrossRef  Google Scholar 

  6. 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

    MATH  CrossRef  Google Scholar 

  7. 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)

    CrossRef  Google Scholar 

  8. Agrawal, K., Benoit, A., Dufossé, F., Robert, Y.: Mapping filtering streaming applications. Algorithmica 62(1–2), 258–308 (2012)

    MathSciNet  MATH  CrossRef  Google Scholar 

  9. 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)

    Google Scholar 

  10. 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)

    CrossRef  Google Scholar 

  11. 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

    CrossRef  Google Scholar 

  12. Brownlee, J.: Clever algorithms: nature-inspired programming recipes (2011)

    Google Scholar 

  13. 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)

    Google Scholar 

  14. 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)

    Google Scholar 

  15. Buzacott, J.A.: Commonalities in reengineered business processes: models and issues. Manag. Sci. 42(5), 768–782 (1996)

    MATH  CrossRef  Google Scholar 

  16. Deshpande, A., Hellerstein, L.: Parallel pipelined filter ordering with precedence constraints. ACM Trans. Algorithms 8(4), 41:1–41:38 (2012)

    Google Scholar 

  17. 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

    CrossRef  Google Scholar 

  18. 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

    CrossRef  Google Scholar 

  19. 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)

    Google Scholar 

  20. 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)

    CrossRef  Google Scholar 

  21. Ibaraki, T., Kameda, T.: On the optimal nesting order for computing N-relational joins. ACM Trans. Database Syst. 9(3), 482–502 (1984)

    MathSciNet  CrossRef  Google Scholar 

  22. 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

  23. 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)

    CrossRef  Google Scholar 

  24. 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

    CrossRef  Google Scholar 

  25. Köpke, J., Franceschetti, M., Eder, J.: Optimizing data-flow implementations for inter-organizational processes. Distrib. Parallel Databases 37, 651–695 (2018)

    CrossRef  Google Scholar 

  26. 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

    CrossRef  Google Scholar 

  27. 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

    CrossRef  Google Scholar 

  28. 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

  29. 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)

    CrossRef  Google Scholar 

  30. Kougka, G., Gounaris, A., Tsichlas, K.: Practical algorithms for execution engine selection in data flows. Future Generation Comp. Syst. 45, 133–148 (2015)

    CrossRef  Google Scholar 

  31. Krishnamurthy, R., Boral, H., Zaniolo, C.: Optimization of nonrecursive queries. In: VLDB, pp. 128–137 (1986)

    Google Scholar 

  32. 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)

    CrossRef  Google Scholar 

  33. 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)

    Google Scholar 

  34. 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

    CrossRef  Google Scholar 

  35. Michailidou, A., Gounaris, A.: Bi-objective traffic optimization in geo-distributed data flows. Big Data Res. 16, 36–48 (2019)

    CrossRef  Google Scholar 

  36. 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)

    CrossRef  Google Scholar 

  37. 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

    CrossRef  Google Scholar 

  38. 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)

    Google Scholar 

  39. Polyvyanyy, A., García-Bañuelos, L., Dumas, M.: Structuring acyclic process models. Inf. Syst. 37(6), 518–538 (2012)

    CrossRef  Google Scholar 

  40. 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)

    CrossRef  Google Scholar 

  41. Pourmasoumi, A., Bagheri, E.: Business process mining. CoRR abs/1607.00607 (2016)

    Google Scholar 

  42. Rheinländer, A., Leser, U., Graefe, G.: Optimization of complex dataflows with user-defined functions. ACM Comput. Surv. 50(3), 38:1–38:39 (2017)

    Google Scholar 

  43. Rosa, M.L., et al.: Managing process model complexity via abstract syntax modifications. IEEE Trans. Ind. Inf. 7(4), 614–629 (2011)

    CrossRef  Google Scholar 

  44. 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)

    CrossRef  Google Scholar 

  45. Schunselaar, D.: Configurable process trees: elicitation, analysis, and enactment (2016)

    Google Scholar 

  46. Simitsis, A., Wilkinson, K., Castellanos, M., Dayal, U.: Optimizing analytic data flows for multiple execution engines. In: SIGMOD Conference, pp. 829–840 (2012)

    Google Scholar 

  47. Simitsis, A., Wilkinson, K., Dayal, U., Castellanos, M.: Optimizing ETL workflows for fault-tolerance. In: ICDE, pp. 385–396 (2010)

    Google Scholar 

  48. 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)

    Google Scholar 

  49. Tsakalidis, G., Vergidis, K., Kougka, G., Gounaris, A.: Eligibility of BPMN models for business process redesign. Information 10(7), 225 (2019)

    CrossRef  Google Scholar 

  50. 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

    CrossRef  Google Scholar 

  51. Varol, Y.L., Rotem, D.: An algorithm to generate all topological sorting arrangements. Comput. J. 24(1), 83–84 (1981)

    MATH  CrossRef  Google Scholar 

  52. 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)

    Google Scholar 

  53. 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)

    Google Scholar 

  54. 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)

    Google Scholar 

  55. Zaharia, M., et al.: Apache spark: a unified engine for big data processing. Commun. ACM 59(11), 56–65 (2016)

    CrossRef  Google Scholar 

  56. 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)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Georgia Kougka, Konstantinos Varvoutas & Anastasios Gounaris

  2. Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece

    George Tsakalidis & Kostas Vergidis

Authors
  1. Georgia Kougka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Konstantinos Varvoutas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anastasios Gounaris
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. George Tsakalidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kostas Vergidis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anastasios Gounaris .

Editor information

Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Prof. Abdelkader Hameurlain

  2. Vienna University of Technology, Vienna, Austria

    Prof. Dr. A Min Tjoa

Rights and permissions

Reprints and Permissions

Copyright information

© 2020 Springer-Verlag GmbH Germany, part of Springer Nature

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-62199-8_3

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-62198-1

  • Online ISBN: 978-3-662-62199-8

  • eBook Packages: Computer ScienceComputer Science (R0)