Skip to main content
SpringerLink
Log in

Ant-Colony Optimisation for Path Recommendation in Business Process Execution

  • Original Article
  • Published:
Journal on Data Semantics

Abstract

In business process management, operational support concerns methods and tools to support users during the execution of business processes. One possible way of supporting users is to suggest the optimal way to complete the execution of a business process instance given the set of activities executed thus far and a notion of utility associated with the execution of possible remaining activities. This problem goes also under the label of process navigation. This paper proposes a novel technique to implement process navigation based on the innovative abstraction of business process models as a restricted class of directed hypergraphs, i.e. WF-hypergraphs. In our approach, workflow net process models are first transformed into WF-hypergraphs. Using this abstraction, finding the optimal way to complete a business process becomes a generalised hypergraph shortest path problem, which is NP-hard. To solve this problem, we propose a solution based on the ant-colony meta-heuristic specifically customised to the case of hypergraph traversal. The paper presents an experimental evaluation of the proposed optimisation heuristic and discusses how the proposed approach can be integrated into modern business process management systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. Note that, with an abuse of notation, we identify the nodes created in step 1 using the corresponding labels in PN.

  2. For a parallel block, the execution time is equal to the longest expected execution time of a branch in the block; for a conditional block, the execution time is equal to the sum of the expected execution time of activities in a block weighted by their respective probability of execution.

  3. Code at: https://github.com/emettelatripla/opsupport.

  4. www.promtools.org.

  5. Logs available at: http://www.win.tue.nl/ieeetfpm/doku.php?id=shared:process_mining_logs.

References

  1. Abdullah L, Adawiyah CR (2014) Simple additive weighting methods of multi criteria decision making and applications: a decade review. Int J Inf Process Manag 5(1):39

    Google Scholar 

  2. Ardagna D, Pernici B (2007) Adaptive service composition in flexible processes. IEEE Trans Softw Eng 33(6):369–384

    Article  Google Scholar 

  3. Ausiello G, Italiano G, Nanni U, Brim L (1998) Hypergraph traversal revisited: cost measures and dynamic algorithms. Math Found Comput Sci 1450:1–16

    MathSciNet  MATH  Google Scholar 

  4. Bae H, Lee S, Moon I (2014) Planning of business process execution in business process management environments. Inf Sci 268:357–369

    Article  Google Scholar 

  5. Ballou DP, Pazer HL (1985) Modeling data and process quality in multi-input, multi-output information systems. Manag Sci 31(2):150–162

    Article  Google Scholar 

  6. Barba I, Weber B, Del Valle C, Jiménez-Ramírez A (2013) User recommendations for the optimized execution of business processes. Data Knowl Eng 86:61–84

    Article  Google Scholar 

  7. Blum C (2005) Beam-ACO—hybridizing ant colony optimization with beam search: an application to open shop scheduling. Comput Oper Res 32:1565–1591

    Article  MATH  Google Scholar 

  8. Cardoso J (2008) Business process control-flow complexity: metric, evaluation, and validation. Int J Web Serv Res 5(2):49–76

    Article  Google Scholar 

  9. Cardoso J, Jablonski S, Volz B (2013) A navigation metaphor to support mobile workflow systems. In: International conference on business process management, Springer, pp 537–548

  10. Chang D-H, Son JH, Kim MH (2002) Critical path identification in the context of a workflow. Inf Softw Technol 44(7):405–417

    Article  Google Scholar 

  11. Comuzzi M (2017) Optimal paths in business processes: framework and applications. In: Business process management workshops

  12. Comuzzi M, Vanderfeesten ITP, Wang T (2013) Optimized cross-organizational business process monitoring: design and enactment. Inf Sci 244:107–118

    Article  Google Scholar 

  13. Conforti R, Augusto A, La Rosa M, Dumas M, Garcia-Banuelos L (2016) BPMN miner 2.0: discovering hierarchical and block-structured BPMN process models. In: International conference on business process management. Springer, pp 328–343

  14. Conforti R, de Leoni M, La Rosa M, van der Aalst WM, ter Hofstede AH (2015) A recommendation system for predicting risks across multiple business process instances. Decis Support Syst 69:1–19

    Article  Google Scholar 

  15. Di Francescomarino C, Dumas M, Federici M, Ghidini C, Maggi FM, Rizzi W (2016) Predictive business process monitoring framework with hyperparameter optimization. In: International conference on advanced information systems engineering, pp 361–376

  16. Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344:243–278

    Article  MathSciNet  MATH  Google Scholar 

  17. Dumas M, La Rosa M, Mendling J, Reijers HA et al (2013) Fundamentals of business process management, vol 1. Springer, Berlin

    Book  Google Scholar 

  18. Dumas M, Van der Aalst WM, Ter Hofstede AH (2005) Process-aware information systems: bridging people and software through process technology. Wiley, Hoboken

    Book  Google Scholar 

  19. Elbeltagi E, Hegazy T, Grierson D (2005) Comparison among five evolutionary-based optimization algorithms. Adv Eng Inform 19:43–53

    Article  Google Scholar 

  20. Fahland D, van der Aalst WMP (2015) Model repair—aligning process models to reality. Inf Syst 47:220–243

    Article  Google Scholar 

  21. Fox B, Xiang W, Lee H (2007) Industrial applications of the ant colony optimization algorithm. Int J Adv Manuf Technol 31:805–814

    Article  Google Scholar 

  22. Gallo G, Longo G, Pallottino S, Nguyen S (1993) Directed hypergraphs and applications. Discrete Appl Math 42(2–3):177–201

    Article  MathSciNet  MATH  Google Scholar 

  23. Ghattas J, Soffer P, Peleg M (2014) Improving business process decision making based on past experience. Decis Support Syst 59:93–107

    Article  Google Scholar 

  24. Ghobadian A, Speller S, Jones M (1994) Service quality: concepts and models. Int J Qual Reliab Manag 11(9):43–66

    Article  Google Scholar 

  25. Ginsberg M (1993) Essentials of artificial intelligence. Morgan Kaufmann Publishers, Burlington

    Google Scholar 

  26. Günther CW, Van Der Aalst WM (2007) Fuzzy mining-adaptive process simplification based on multi-perspective metrics. In: International conference on business process management, Springer, pp 328–343

  27. Haisjackl C, Weber B (2010) User assistance during process execution-an experimental evaluation of recommendation strategies. In: International conference on business process management, pp 134–145

  28. Huang Z, Lu X, Duan H (2012) Using recommendation to support adaptive clinical pathways. J Med Syst 36(3):1849–1860

    Article  Google Scholar 

  29. Laguna M, Marklund J (2013) Business process modeling, simulation and design. CRC Press, Boca Raton

    Google Scholar 

  30. Lakshmanan GT, Shamsi D, Doganata YN, Unuvar M, Khalaf R (2015) A markov prediction model for data-driven semi-structured business processes. Knowl Inf Syst 42(1):97–126

    Article  Google Scholar 

  31. Leemans SJ, Fahland D, van der Aalst WM (2013) Discovering block-structured process models from event logs containing infrequent behaviour. In: International conference on business process management, Springer, pp 66–78

  32. Maggi FM, Di Francescomarino C, Dumas M, Ghidini C (2014) Predictive monitoring of business processes. In: International conference on advanced information systems engineering, pp 457–472

  33. Mrquez-Chamorro AE, Resinas M, Ruiz-Cortes A (2017) Predictive monitoring of business processes: a survey. IEEE Trans Serv Comput 1:1–1

    Google Scholar 

  34. Oh J, Cho NW, Kim H, Min Y, Kang S-H (2011) Dynamic execution planning for reliable collaborative business processes. Inf Sci 181(2):351–361

    Article  Google Scholar 

  35. Polyvyabyy A, Weske M (2009) Hypergraph-based modeling of ad-hoc business processes. In: BPM workshops 2008, pp 278–289. Springer

  36. Polyvyanyy A, Smirnov S, Weske M (2015) Business process model abstraction. Handbook on business process management, vol 1. Springer, Berlin, pp 147–165

    Chapter  Google Scholar 

  37. Rogge-Solti A, Weske M (2015) Prediction of business process durations using non-Markovian stochastic Petri nets. Inf Syst 54:1–14

    Article  Google Scholar 

  38. Schonenberg H, Weber B, Van Dongen B, Van der Aalst W (2008) Supporting flexible processes through recommendations based on history. In: International conference on business process management. Springer, pp 51–66

  39. Sim K, Sun W (2003) Ant colony optimization for routing and load-balancing: survey and new directions. IEEE Trans Syst Sci Cybern Part A 33:560–572

    Article  Google Scholar 

  40. Song W, Xia X, Jacobsen H-A, Zhang P, Hu H (2017) Efficient alignment between event logs and process models. IEEE Trans Serv Comput 10(1):136–149

    Article  Google Scholar 

  41. Thakur M, Tripathi R (2009) Linear connectivity problems in directed hypergraphs. Theor Comput Sci 410:2592–2618

    Article  MathSciNet  MATH  Google Scholar 

  42. van Aalst WM, van Hee KM, van Werf JM, Verdonk M (2010) Auditing 2.0: using process mining to support tomorrow’s auditor. Computer 43(3):90–93

    Article  Google Scholar 

  43. Van Der Aalst W (2012) Process mining: overview and opportunities. ACM Trans Manag Inf Syst 3(2):7

    Google Scholar 

  44. van der Aalst WM (2009) Tomtom for business process management (tomtom4bpm). In: International conference on advanced information systems engineering, pp 2–5

  45. Van der Aalst WM, Schonenberg MH, Song M (2011) Time prediction based on process mining. Inf Syst 36(2):450–475

    Article  Google Scholar 

  46. van der Aalst WMP, van Hee KM, ter Hofstede AHM, Sidorova N, Verbeek HMW, Voorhoeve M, Wynn MT (2010) Soundness of workflow nets: classification, decidability, and analysis. Form Asp Comput 23(3):333–363

    Article  MathSciNet  MATH  Google Scholar 

  47. van der Aalst WMP, van Hee KM, ter Hofstede AHM, Sidorova N, Verbeek HMW, Voorhoeve M, Wynn MT (2011) Soundness of workflow nets: classification, decidability, and analysis. Form Asp Comput 23:333–363

    Article  MathSciNet  MATH  Google Scholar 

  48. Vanderfeesten I, Reijers HA, van der Aalst WM (2011) Product-based workflow support. Inf Syst 36(2):517–535

    Article  Google Scholar 

  49. Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci 46(2):186–204

    Article  Google Scholar 

  50. Wang J, Song S, Zhu X, Lin X, Sun J (2016) Efficient recovery of missing events. IEEE Trans Knowl Data Eng 28(11):2943–2957

    Article  Google Scholar 

  51. Winston WL, Goldberg JB (2004) Operations research: applications and algorithms, vol 3. Duxbury Press, Belmont

    Google Scholar 

Download references

Acknowledgements

The work presented in this paper was supported by NRF Korea (Project No. 2017076589).

Author information

Authors and Affiliations

  1. Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea

    Marco Comuzzi

Authors
  1. Marco Comuzzi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marco Comuzzi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Comuzzi, M. Ant-Colony Optimisation for Path Recommendation in Business Process Execution. J Data Semant 8, 113–128 (2019). https://doi.org/10.1007/s13740-018-0099-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13740-018-0099-x

Keywords

Search

Navigation